In [1]:
from pathlib import Path
from copy import deepcopy
import numpy as np
import matplotlib as mp
import matplotlib.pyplot as plt
import seaborn as sns
import scanpy as sc
import squidpy as sq
import pandas as pd
import anndata as ad
import scipy as sp
from scipy.spatial import distance_matrix
#import commot as ct
from scipy.stats import ttest_ind
from scipy.sparse import csr_array, tril
sc.logging.print_header()
/opt/anaconda3/envs/spatial_new/lib/python3.9/site-packages/numba/core/decorators.py:246: RuntimeWarning: nopython is set for njit and is ignored
warnings.warn('nopython is set for njit and is ignored', RuntimeWarning)
scanpy==1.10.2 anndata==0.10.9 umap==0.5.6 numpy==1.23.2 scipy==1.13.1 pandas==2.2.3 scikit-learn==1.5.1 statsmodels==0.14.2 igraph==0.11.6 pynndescent==0.5.13
Pre-processing¶
In [2]:
nanostring_dir = Path('/Users/paolamaragno/Library/CloudStorage/OneDrive-UniversitàdegliStudidiMilano/Policlinico/analisi/spatial/spatial/K/').resolve()
sample_dir = nanostring_dir
In [5]:
adata = sq.read.nanostring(
path=sample_dir,
counts_file="R5941_ColonTMA_exprMat_file_K.csv",
meta_file="R5941_ColonTMA_metadata_file_K.csv",
fov_file="R5941_ColonTMA_fov_positions_file_K.csv",
)
In [6]:
# Obtain the control probes using their names prefixed with “NegPrb-“.
adata.var["NegPrb"] = adata.var_names.str.startswith("NegPrb")
sc.pp.calculate_qc_metrics(adata, qc_vars=["NegPrb"], inplace=True)
In [7]:
adata.obs["total_counts_NegPrb"].sum() / adata.obs["total_counts"].sum() * 100
#The percentage of unassigned “NegPrb” transcripts can be calculated from the calculated qc metrics. This can later be used to estimate
# false discovery rate.
Out[7]:
0.14192144649736035
In [8]:
t = ['NegPrb01',
'NegPrb02',
'NegPrb03',
'NegPrb04',
'NegPrb05',
'NegPrb06',
'NegPrb07',
'NegPrb08',
'NegPrb09',
'NegPrb10']
In [9]:
adata = adata[:,~adata.var.index.isin(t)]
In [9]:
exp = pd.DataFrame(adata.X.toarray(),index = adata.obs.index.values, columns = adata.var.index.values)
gene_cell = pd.DataFrame(index = exp.columns, columns = ['count'])
for g in exp.columns:
gene_cell.loc[g] = len(exp.loc[:,g][exp.loc[:,g]!=0])
In [10]:
np.min(gene_cell.loc[:,'count'])
Out[10]:
332
In [11]:
fig, axs = plt.subplots(1, 4, figsize=(17, 4))
axs[0].set_title("Total transcripts per cell")
sns.histplot(
adata.obs["total_counts"],
kde=False,
ax=axs[0],
)
axs[0].set_xlim(0, 500)
axs[1].set_title("Unique transcripts per cell")
sns.histplot(
adata.obs["n_genes_by_counts"],
kde=False,
ax=axs[1],
)
axs[2].set_title("Transcripts per FOV")
sns.histplot(
adata.obs.groupby("fov").sum()["total_counts"],
kde=False,
bins =20,
ax=axs[2],
)
axs[3].set_title("Number of cells expressing same transcript")
sns.histplot(
gene_cell['count'],
kde=False,
bins =60,
ax=axs[3]
)
axs[3].set(xlabel='Number of cells expressing x transcripts')
axs[3].set_xlim(0, 5000)
plt.savefig('/Users/paolamaragno/Library/CloudStorage/OneDrive-UniversitàdegliStudidiMilano/Policlinico/analisi/spatial/spatial/K/results_seed/quality.pdf',bbox_inches="tight")
/var/folders/xx/sgggrhp55xx_8s1thvzkgr600000gn/T/ipykernel_5208/1594851855.py:20: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
adata.obs.groupby("fov").sum()["total_counts"],
In [10]:
# Filter the cells based on the minimum number of counts required using scanpy.pp.filter_cells. Filter the genes based on the minimum
# number of cells required with scanpy.pp.filter_genes. The parameters for both were specified based on the plots above. This filtering
# is quite conservative, more relaxed settings might also be applicable. Other criteria for filtering cells could be area,
# immunofluorescence signal or number of unique transcripts.
sc.pp.filter_cells(adata, min_counts=25)
#sc.pp.filter_genes(adata, min_cells=700)
/opt/anaconda3/envs/spatial_new/lib/python3.9/site-packages/scanpy/preprocessing/_simple.py:164: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual. adata.obs["n_counts"] = number
In [11]:
# Normalize counts per cell
sc.pp.normalize_total(adata, inplace = True)
sc.pp.log1p(adata)
# PCA
sc.pp.pca(adata, random_state=10)
# compute a beigthorhood graph
sc.pp.neighbors(adata, random_state=10)
# embed the neighborhood graph of the data
sc.tl.umap(adata, random_state=10)
# cluster the cells into subgroups
sc.tl.leiden(adata, resolution=1.2, random_state=10)
/var/folders/xx/sgggrhp55xx_8s1thvzkgr600000gn/T/ipykernel_2817/1192101804.py:16: FutureWarning: In the future, the default backend for leiden will be igraph instead of leidenalg. To achieve the future defaults please pass: flavor="igraph" and n_iterations=2. directed must also be False to work with igraph's implementation. sc.tl.leiden(adata, resolution=1.2, random_state=10)
In [11]:
num_cells_per_patient = {}
for p in pd.unique(adata.obs.Patients):
num_cells_per_patient[p] = adata[adata.obs.Patients == p].shape[0]
In [15]:
num_cells_per_patient
Out[15]:
{'Patient_1': 577,
'Patient_2': 3291,
'Patient_4': 3877,
'Patient_6': 6311,
'Patient_7': 1257,
'Patient_8': 6108,
'Patient_9': 5336}
In [12]:
num_cells_per_fov = {}
for f in pd.unique(adata.obs.fov):
num_cells_per_fov[f] = adata[adata.obs.fov == f].shape[0]
In [13]:
num_cells_per_fov
Out[13]:
{'1': 577,
'4': 1275,
'5': 2016,
'6': 1597,
'7': 1172,
'8': 1108,
'9': 2473,
'10': 1918,
'11': 1920,
'12': 825,
'13': 432,
'14': 2194,
'16': 1753,
'17': 2161,
'18': 1758,
'19': 1905,
'20': 1673}
In [12]:
palette = ['#0F2080', '#006600', '#AA3377', '#BBBBBB', '#AEC7E8', '#CCBB44', '#F7B6D2','#000000',
'#0077BB', '#EE7733', '#33BBEE', '#CC3311', '#7F7F7F', '#FFB000','#601A4A','#785EF0','#66CC00', '#CCFF99',
'#660000', '#E5CCFF','#FFFFCC','#CCFFE5']
sns.color_palette(palette)
Out[12]:
In [13]:
sc.pl.umap(
adata, show=False,
color= "leiden", palette=sns.color_palette(palette)
)
plt.savefig('/Users/paolamaragno/Library/CloudStorage/OneDrive-UniversitàdegliStudidiMilano/Policlinico/analisi/spatial/spatial/K/results_seed/umap_clusters_not_named_before_subclustering.pdf',bbox_inches="tight")
In [19]:
cat_name = "leiden"
sig_leiden = pd.DataFrame(
columns=adata.var_names, index=adata.obs[cat_name].cat.categories
)
# for each cluster, extract the average expression of each gene in the cells of that cluster
for clust in adata.obs[cat_name].cat.categories:
sig_leiden.loc[clust] = adata[adata.obs[cat_name].isin([clust]), :].X.mean(0)
# transpose this matrix so that the columns are the clusters and the rows are the cosmx genes
sig_leiden = sig_leiden.transpose()
sig_leiden.to_excel('/Users/paolamaragno/Library/CloudStorage/OneDrive-UniversitàdegliStudidiMilano/Policlinico/analisi/spatial/spatial/K/results_seed/gene_counts_all_clusters_non_named_before_subclustering.xlsx')
In [20]:
num_top_genes = 50
l = list()
for inst_cluster in sig_leiden.columns.tolist():
# for each leiden cluster (column names of sig_leiden dataframe), sort the genes in decreasing order of expression and keep the 30 genes with
# highest expression.
top_genes = (
sig_leiden[inst_cluster]
.sort_values(ascending=False)
.index.tolist()[:num_top_genes]
)
l.append(inst_cluster)
l.append((sig_leiden[inst_cluster][top_genes].to_string()))
with open('/Users/paolamaragno/Library/CloudStorage/OneDrive-UniversitàdegliStudidiMilano/Policlinico/analisi/spatial/spatial/K/results_seed/top_50_genes_clusters_non_named_before_subclustering.txt', 'w') as fp:
for item in l:
# write each item on a new line
fp.write("%s\n" % item)
fp.write("\n\n")
In [ ]:
# FCGBP (0,13), AGR2 (0,13) -> goblet
# CD24 (1,4), LYZ (4,17), SERPINA1 (4) -> paneth
# COL1A1 (8,12,3,11,7), DCN (12,11,3) -> fibroblast
# INHBA (8,7,3,11),
# CXCL14 (7,12) -> CAF CXCL14
# PDGFRB (10) -> pericyte
# PECAM1 (2), CD93 (2), TIE1 (2) -> endothelial
# IGKC (6), IGHG1 (7), IGHA1 (7) -> plasma
# MS4A1 (11), CD19 (11) -> B
# CD68 (15), CD163 (14,9), CD14 (14) -> macrophage
# CD3D (11), CD3E (11), CD3G (11), CD2 (3,9,5), CCL5 (5,3) -> T
# MYH11 (10,7), ACTG2 (10,7) -> smooth
# EPCAM (1,4,16,0,13) -> epithelial
# KIT (0,17), TPSAB1.B2 (11), CPA3 (11) -> mast cells
In [147]:
sig_leiden.loc['CD14',].sort_values(ascending=False)
Out[147]:
14 0.468328 3 0.340124 17 0.207969 12 0.099846 2 0.095585 9 0.070249 6 0.062449 16 0.060485 18 0.059438 5 0.053112 4 0.050071 8 0.048272 10 0.040609 7 0.03958 1 0.036582 11 0.035131 15 0.032177 0 0.029749 13 0.018915 Name: CD14, dtype: object
In [14]:
adata_subset = adata[adata.obs.leiden.isin(['2','4','9'])].copy()
# Normalize counts per cell
sc.pp.normalize_total(adata_subset, inplace = True)
# logarithmize
sc.pp.log1p(adata_subset)
# PCA
sc.pp.pca(adata_subset, random_state=10)
# compute a beigthorhood graph
sc.pp.neighbors(adata_subset, random_state=10)
# embed the neighborhood graph of the data
sc.tl.umap(adata_subset, random_state=10)
# cluster the cells into subgroups
sc.tl.leiden(adata_subset, resolution=1, random_state=10)
WARNING: adata.X seems to be already log-transformed.
In [15]:
sc.pl.umap(
adata_subset, show=False,
color= "leiden"
)
Out[15]:
<Axes: title={'center': 'leiden'}, xlabel='UMAP1', ylabel='UMAP2'>
In [23]:
cat_name = "leiden"
sig_leiden2 = pd.DataFrame(
columns=adata_subset.var_names, index=adata_subset.obs[cat_name].cat.categories
)
# for each cluster, extract the average expression of each gene in the cells of that cluster
for clust in adata_subset.obs[cat_name].cat.categories:
sig_leiden2.loc[clust] = adata_subset[adata_subset.obs[cat_name].isin([clust]), :].X.mean(0)
# transpose this matrix so that the columns are the clusters and the rows are the cosmx genes
sig_leiden2 = sig_leiden2.transpose()
sig_leiden2.to_excel('/Users/paolamaragno/Library/CloudStorage/OneDrive-UniversitàdegliStudidiMilano/Policlinico/analisi/spatial/spatial/K/results_seed/gene_counts_all_clusters_non_named_only_2_4_9.xlsx')
In [24]:
num_top_genes = 50
l = list()
for inst_cluster in sig_leiden2.columns.tolist():
# for each leiden cluster (column names of sig_leiden dataframe), sort the genes in decreasing order of expression and keep the 30 genes with
# highest expression.
top_genes = (
sig_leiden2[inst_cluster]
.sort_values(ascending=False)
.index.tolist()[:num_top_genes]
)
l.append(inst_cluster)
l.append((sig_leiden2[inst_cluster][top_genes].to_string()))
with open('/Users/paolamaragno/Library/CloudStorage/OneDrive-UniversitàdegliStudidiMilano/Policlinico/analisi/spatial/spatial/K/results_seed/top_50_genes_clusters_non_named_only_2_4_9.txt', 'w') as fp:
for item in l:
# write each item on a new line
fp.write("%s\n" % item)
fp.write("\n\n")
In [256]:
sig_leiden2.loc['CD14',].sort_values(ascending=False)
# endo: 4,1,5
# pericyte: 6
# muscle: 3,2,0
Out[256]:
9 0.223353 7 0.061879 3 0.055938 0 0.048815 2 0.04434 8 0.039805 1 0.034964 4 0.033241 6 0.027757 5 0.017733 10 0.0 Name: CD14, dtype: object
In [16]:
adata_subset.obs.leiden.cat.rename_categories(['20', '21', '22', '23','24','25', '26','27','28','29','30'])
map_dict = {'0':'20', '1':'21', '2':'22', '3':'23', '4':'24', '5':'25','6':'26','7':'27','8':'28','9':'29','10':'30'}
adata_subset.obs['new_leiden'] = adata_subset.obs['leiden'].map(map_dict).astype('category')
adata_subset.obs.new_leiden
Out[16]:
1_1 20
3_1 21
4_1 20
8_1 24
12_1 20
..
1662_20 25
1679_20 23
1684_20 24
1697_20 25
1700_20 24
Name: new_leiden, Length: 5854, dtype: category
Categories (11, object): ['20', '21', '22', '23', ..., '27', '28', '29', '30']
In [26]:
adata.obs.leiden.to_csv('/Users/paolamaragno/Library/CloudStorage/OneDrive-UniversitàdegliStudidiMilano/Policlinico/analisi/spatial/spatial/K/results_seed/old_2_4_9.csv')
adata_subset.obs.new_leiden.to_csv('/Users/paolamaragno/Library/CloudStorage/OneDrive-UniversitàdegliStudidiMilano/Policlinico/analisi/spatial/spatial/K/results_seed/reclust_2_4_9.csv')
In [17]:
new = pd.read_csv('/Users/paolamaragno/Library/CloudStorage/OneDrive-UniversitàdegliStudidiMilano/Policlinico/analisi/spatial/spatial/K/results_seed/merge_2_4_9.csv', index_col=0)
adata2 = adata.copy()
adata2.obs['new_leiden'] = new
adata2.obs['new_leiden'] = adata2.obs['new_leiden'].astype("category")
In [18]:
palette = ['#0F2080', '#006600', '#AA3377', '#BBBBBB', '#AEC7E8', '#CCBB44', '#F7B6D2','#000000',
'#0077BB', '#EE7733', '#33BBEE', '#CC3311', '#7F7F7F', '#FFB000','#601A4A','#785EF0','#66CC00', '#CCFF99',
'#EE6677','#009988','#F0E442', '#FFFF66',"#666600", '#994C00', '#E5CCFF','#FFFFCC','#CCFFE5']
sns.color_palette(palette)
Out[18]:
In [19]:
sc.pl.umap(
adata2, show=False,
color= "new_leiden", palette=sns.color_palette(palette)
)
plt.savefig('/Users/paolamaragno/Library/CloudStorage/OneDrive-UniversitàdegliStudidiMilano/Policlinico/analisi/spatial/spatial/K/results_seed/umap_clusters_not_named_after_subclustering.pdf',bbox_inches="tight")
In [20]:
map_dict = {0:'Goblet', 1:'Epithelial', 3:'Macrophage', 5:'CAF',6:'Smooth muscle',
7:'CAF CXCL14',8:'Endothelial',10:'CAF',11:'Pericyte',12:'CAF',13:'Paneth',
14:'Macrophage',15:'Paneth',16:'Epithelial',17:'Goblet',18:'Goblet',20:'Not annotated',
21:'T',22:'Fibroblast',23:'B',24:'T',25:'Plasma IgG',26:'Plasma IgG',27:'Monocyte',
28:'Plasma IgA',29:'Macrophage',30:'Mast cell'}
adata2.obs['leiden'] = adata2.obs['new_leiden'].map(map_dict).astype('category')
adata2.obs.leiden
# endo: 4,1,5
# pericyte: 6
# muscle: 3,2,0
Out[20]:
1_1 Not annotated
2_1 Macrophage
3_1 T
4_1 Not annotated
5_1 Macrophage
...
1695_20 CAF CXCL14
1696_20 CAF CXCL14
1697_20 Plasma IgG
1698_20 Epithelial
1700_20 T
Name: leiden, Length: 26757, dtype: category
Categories (17, object): ['B', 'CAF', 'CAF CXCL14', 'Endothelial', ..., 'Plasma IgA', 'Plasma IgG', 'Smooth muscle', 'T']
In [31]:
cat_name = "new_leiden"
sig_leiden = pd.DataFrame(
columns=adata2.var_names, index=adata2.obs[cat_name].cat.categories
)
# for each cluster, extract the average expression of each gene in the cells of that cluster
for clust in adata2.obs[cat_name].cat.categories:
sig_leiden.loc[clust] = adata2[adata2.obs[cat_name].isin([clust]), :].X.mean(0)
# transpose this matrix so that the columns are the clusters and the rows are the cosmx genes
sig_leiden = sig_leiden.transpose()
sig_leiden.to_excel('/Users/paolamaragno/Library/CloudStorage/OneDrive-UniversitàdegliStudidiMilano/Policlinico/analisi/spatial/spatial/K/results_seed/gene_counts_all_clusters_not_named_after_subsampling.xlsx')
In [32]:
cat_name = "leiden"
sig_leiden = pd.DataFrame(
columns=adata2.var_names, index=adata2.obs[cat_name].cat.categories
)
# for each cluster, extract the average expression of each gene in the cells of that cluster
for clust in adata2.obs[cat_name].cat.categories:
sig_leiden.loc[clust] = adata2[adata2.obs[cat_name].isin([clust]), :].X.mean(0)
# transpose this matrix so that the columns are the clusters and the rows are the cosmx genes
sig_leiden = sig_leiden.transpose()
sig_leiden.to_excel('/Users/paolamaragno/Library/CloudStorage/OneDrive-UniversitàdegliStudidiMilano/Policlinico/analisi/spatial/spatial/K/results_seed/gene_counts_all_clusters_named.xlsx')
In [21]:
palette = ['#FFB000','#0F2080', '#228833', '#AA3377', '#BBBBBB', '#AEC7E8', '#CCBB44', '#F7B6D2',
'#000000', '#33BBEE', '#CC3311', '#666600','#FFFF66','#CCFF99','#FF33FF','#0077BB','#994C00']
sns.color_palette(palette)
Out[21]:
In [22]:
sc.pl.umap(
adata2, show=False,
color= "leiden", palette=sns.color_palette(palette)
)
plt.savefig('/Users/paolamaragno/Library/CloudStorage/OneDrive-UniversitàdegliStudidiMilano/Policlinico/analisi/spatial/spatial/K/results_seed/umap_clusters_named.pdf',bbox_inches="tight")
In [23]:
level = []
for p in adata2.obs.Patients:
if p in ['Patient_1', 'Patient_6','Patient_8',
'Patient_9']:
level.append('LOW')
else:
level.append('HIGH')
In [24]:
adata2.obs['level'] = level
adata2.obs['level'] = adata2.obs['level'].astype("category")
In [22]:
disease = []
for p in adata2.obs.Patients:
if p in ['Patient_2', 'Patient_7', 'Patient_9']:
disease.append('K_UC')
else:
disease.append('K_CD')
In [23]:
adata2.obs['disease'] = disease
adata2.obs['disease'] = adata2.obs['disease'].astype("category")
In [24]:
sig_leiden.loc['ZBTB16',].sort_values(ascending=False)
Out[24]:
Pericyte 0.351168 Smooth muscle 0.172495 Fibroblast 0.12196 Endothelial 0.102752 T 0.05954 B 0.058132 Macrophage 0.040627 CAF 0.035109 Plasma IgA 0.027984 Goblet 0.025203 Epithelial 0.025047 Plasma IgG 0.019067 CAF CXCL14 0.0181 Paneth 0.017649 Name: ZBTB16, dtype: object
In [39]:
T_CAF = []
for c in adata2.obs.leiden:
if c in ['T','CAF', 'CAF CXCL14']:
T_CAF.append(c)
else:
T_CAF.append('other')
In [40]:
adata2.obs['T_CAF'] = T_CAF
adata2.obs['T_CAF'] = adata2.obs['T_CAF'].astype("category")
In [39]:
sc.pl.umap(
adata2, show=False,
color= "disease", palette=sns.color_palette(['#14E14B','#3F09F0'])
)
plt.savefig('/Users/paolamaragno/Library/CloudStorage/OneDrive-UniversitàdegliStudidiMilano/Policlinico/analisi/spatial/spatial/K/results_seed/umap_disease.pdf',bbox_inches="tight")
In [40]:
sc.pl.umap(
adata2, show=False,
color= "Patients", palette=sns.color_palette(palette)
)
plt.savefig('/Users/paolamaragno/Library/CloudStorage/OneDrive-UniversitàdegliStudidiMilano/Policlinico/analisi/spatial/spatial/K/results_seed/umap_patient.pdf',bbox_inches="tight")
In [41]:
sc.pl.umap(
adata2, show=False,
color= "level", palette=sns.color_palette(['#30ABF2','#E5CCFF'])
)
plt.savefig('/Users/paolamaragno/Library/CloudStorage/OneDrive-UniversitàdegliStudidiMilano/Policlinico/analisi/spatial/spatial/K/results_seed/umap_level.pdf',bbox_inches="tight")
In [41]:
adata2.obs.to_csv('/Users/paolamaragno/Library/CloudStorage/OneDrive-UniversitàdegliStudidiMilano/Policlinico/analisi/spatial/spatial/K/results_seed/metadata_adata.csv')
In [42]:
adata2.var.to_csv("/Users/paolamaragno/Library/CloudStorage/OneDrive-UniversitàdegliStudidiMilano/Policlinico/analisi/spatial/spatial/K/results_seed/gene_names_adata.csv")
In [43]:
fov_names = adata2.obs['fov']
fov_names = adata2.obs['fov']
fov_names = fov_names.astype("string")
patients_names = adata2.obs['Patients']
patients_names = patients_names.astype("string")
level_names = adata2.obs['level']
level_names = level_names.astype("string")
In [81]:
list(set('fov ' + fov_names + ' - ' + patients_names + ' - ' + level_names))
Out[81]:
['fov 14 - Patient_8 - LOW', 'fov 8 - Patient_4 - HIGH', 'fov 20 - Patient_9 - LOW', 'fov 16 - Patient_8 - LOW', 'fov 5 - Patient_2 - HIGH', 'fov 12 - Patient_7 - HIGH', 'fov 1 - Patient_1 - LOW', 'fov 9 - Patient_6 - LOW', 'fov 10 - Patient_6 - LOW', 'fov 17 - Patient_8 - LOW', 'fov 18 - Patient_9 - LOW', 'fov 4 - Patient_2 - HIGH', 'fov 19 - Patient_9 - LOW', 'fov 11 - Patient_6 - LOW', 'fov 6 - Patient_4 - HIGH', 'fov 7 - Patient_4 - HIGH', 'fov 13 - Patient_7 - HIGH']
In [64]:
adata2.uns['spatial'].keys()
Out[64]:
dict_keys(['1', '10', '11', '12', '13', '14', '16', '17', '18', '19', '20', '4', '5', '6', '7', '8', '9'])
In [44]:
t = ['fov 1 - Patient_1 - LOW',
'fov 10 - Patient_6 - LOW',
'fov 11 - Patient_6 - LOW',
'fov 12 - Patient_7 - HIGH',
'fov 13 - Patient_7 - HIGH',
'fov 14 - Patient_8 - LOW',
'fov 16 - Patient_8 - LOW',
'fov 17 - Patient_8 - LOW',
'fov 18 - Patient_9 - LOW',
'fov 19 - Patient_9 - LOW',
'fov 20 - Patient_9 - LOW',
'fov 4 - Patient_2 - HIGH',
'fov 5 - Patient_2 - HIGH',
'fov 6 - Patient_4 - HIGH',
'fov 7 - Patient_4 - HIGH',
'fov 8 - Patient_4 - HIGH',
'fov 9 - Patient_6 - LOW']
In [45]:
sq.pl.spatial_segment(
adata2,
color="leiden",
library_key="fov",
seg_cell_id="cell_ID",
seg_outline=True,
img=False,
title=t,
scalebar_dx=1.0,
scalebar_kwargs={"scale_loc": "bottom", "location": "lower right"},
)
plt.savefig('/Users/paolamaragno/Library/CloudStorage/OneDrive-UniversitàdegliStudidiMilano/Policlinico/analisi/spatial/spatial/K/results_seed/fov_patients_level_senza_back.pdf',bbox_inches="tight")
In [43]:
fig, ax = plt.subplots(2, 2, figsize=(21, 14))
sq.pl.spatial_segment(
adata2,
shape="hex",
color="leiden",
library_key="fov",
library_id="1",
seg_cell_id="cell_ID",
img=False,
scalebar_dx=1.0,
size=60,
ax=ax[0][0],
title='FOV 1 - Patient 1 - low iNKT22'
)
sq.pl.spatial_segment(
adata2,
color="leiden",
library_key="fov",
library_id="4",
seg_cell_id="cell_ID",
img=False,
scalebar_dx=1.0,
size=60,
ax=ax[0][1],
title='FOV 4 - Patient 2 - high iNKT22'
)
sq.pl.spatial_segment(
adata2,
color="T_CAF",
library_key="fov",
library_id="1",
seg_cell_id="cell_ID",
scalebar_dx=1.0,
img=False,
size=60,
ax=ax[1][0], palette=mp.colors.ListedColormap(['#30ABF2','#C0C0C0','#E1DA14','#FFFFCC']),
title='FOV 1 - Patient 1 - low iNKT22. CAF and T cells'
)
sq.pl.spatial_segment(
adata2,
color="T_CAF",
library_key="fov",
library_id="4",
seg_cell_id="cell_ID",
img=False,
size=60,
scalebar_dx=1.0,
ax=ax[1][1], palette=mp.colors.ListedColormap(['#30ABF2','#C0C0C0','#E1DA14','#FFFFCC']),
title='FOV 4 - Patient 2 - high iNKT22. CAF and T cells'
)
plt.savefig('/Users/paolamaragno/Library/CloudStorage/OneDrive-UniversitàdegliStudidiMilano/Policlinico/analisi/spatial/spatial/K/results_K/fov_1_low_4_high.pdf',bbox_inches="tight")
DEGs with scanpy¶
In [46]:
clusters = ["T","B",'Plasma IgA',"Plasma IgG","Macrophage","Mast cell","Monocyte","Smooth muscle","Pericyte", "Endothelial",
"Epithelial","Paneth", "Goblet","Fibroblast","CAF","CAF CXCL14", 'Not annotated']
adata2.obs["clusters_ordered"] = pd.Categorical(
values=adata2.obs.leiden, categories=clusters, ordered=True
)
In [47]:
markers = ['CD3D', 'CD3E', 'CD3G', 'CCL5', 'CD2', 'CD19', 'MS4A1',
'IGKC', 'IGHG1','IGHA1',
'CD68', 'CD163', 'CD14','CPA3','KIT','TPSAB1.B2','S100A8','MYH11', 'ACTG2', 'ACTA2', 'TAGLN', 'PDGFRB',
'PECAM1', 'CD93', 'TIE1','EPCAM', 'LYZ','FCGBP', 'AGR2',
'COL1A1', 'DCN', 'INHBA', 'CXCL14']
sc.pl.dotplot(adata2, markers, groupby='clusters_ordered', standard_scale="var", save=True)
WARNING: saving figure to file figures/dotplot_.pdf
In [60]:
sc.pl.heatmap(adata2, var_names=markers, groupby='clusters_ordered',show_gene_labels=True, save = 'expression_heatmap.pdf',figsize=(10,7),cmap='Reds')
WARNING: saving figure to file figures/heatmapexpression_heatmap.pdf
In [61]:
mp = sc.pl.matrixplot(adata2, markers, 'clusters_ordered', return_fig=True,figsize=(12,5))
mp.add_totals().style(edge_color='black',cmap='Reds')
mp.savefig('/Users/paolamaragno/Library/CloudStorage/OneDrive-UniversitàdegliStudidiMilano/Policlinico/analisi/spatial/spatial/K/results_seed/matrixplot.pdf',bbox_inches="tight")
DEGs between cells of the same cell type, high vs low iNKT22 level¶
In [74]:
pd.unique(adata2.obs.leiden)
Out[74]:
['Not annotated', 'Macrophage', 'T', 'CAF', 'Paneth', ..., 'Plasma IgG', 'B', 'Smooth muscle', 'Mast cell', 'Plasma IgA'] Length: 17 Categories (17, object): ['B', 'CAF', 'CAF CXCL14', 'Endothelial', ..., 'Plasma IgA', 'Plasma IgG', 'Smooth muscle', 'T']
In [20]:
adata_B = adata2[adata2.obs.leiden =='B']
adata_CAF = adata2[adata2.obs.leiden =='CAF']
adata_CAF_CXCL14 = adata2[adata2.obs.leiden =='CAF CXCL14']
adata_endo = adata2[adata2.obs.leiden =='Endothelial']
adata_epi = adata2[adata2.obs.leiden =='Epithelial']
adata_fibro = adata2[adata2.obs.leiden =='Fibroblast']
adata_goblet = adata2[adata2.obs.leiden =='Goblet']
adata_macro = adata2[adata2.obs.leiden =='Macrophage']
adata_mast = adata2[adata2.obs.leiden =='Mast cell']
adata_mono = adata2[adata2.obs.leiden =='Monocyte']
adata_paneth = adata2[adata2.obs.leiden =='Paneth']
adata_peri = adata2[adata2.obs.leiden =='Pericyte']
adata_plasma_A = adata2[adata2.obs.leiden =='Plasma IgA']
adata_plasma_G = adata2[adata2.obs.leiden =='Plasma IgG']
adata_muscle = adata2[adata2.obs.leiden =='Smooth muscle']
adata_T = adata2[adata2.obs.leiden =='T']
In [21]:
sc.tl.rank_genes_groups(adata_B, 'level', groups=['LOW'], reference='HIGH', method='wilcoxon')
df_adata_B = sc.get.rank_genes_groups_df(adata_B, group='LOW',key = "rank_genes_groups")
df_adata_B.to_excel('/Users/paolamaragno/Library/CloudStorage/OneDrive-UniversitàdegliStudidiMilano/Policlinico/analisi/spatial/spatial/K/results_seed/DEGs_B_low_high.xlsx')
sc.tl.rank_genes_groups(adata_CAF, 'level', groups=['LOW'], reference='HIGH', method='wilcoxon')
df_adata_CAF = sc.get.rank_genes_groups_df(adata_CAF, group='LOW',key = "rank_genes_groups")
df_adata_CAF.to_excel('/Users/paolamaragno/Library/CloudStorage/OneDrive-UniversitàdegliStudidiMilano/Policlinico/analisi/spatial/spatial/K/results_seed/DEGs_CAF_low_high.xlsx')
sc.tl.rank_genes_groups(adata_CAF_CXCL14, 'level', groups=['LOW'], reference='HIGH', method='wilcoxon')
df_adata_CAF_CXCL14 = sc.get.rank_genes_groups_df(adata_CAF_CXCL14, group='LOW',key = "rank_genes_groups")
df_adata_CAF_CXCL14.to_excel('/Users/paolamaragno/Library/CloudStorage/OneDrive-UniversitàdegliStudidiMilano/Policlinico/analisi/spatial/spatial/K/results_seed/DEGs_CAF_CXCL14_low_high.xlsx')
sc.tl.rank_genes_groups(adata_endo, 'level', groups=['LOW'], reference='HIGH', method='wilcoxon')
df_adata_endo = sc.get.rank_genes_groups_df(adata_endo, group='LOW',key = "rank_genes_groups")
df_adata_endo.to_excel('/Users/paolamaragno/Library/CloudStorage/OneDrive-UniversitàdegliStudidiMilano/Policlinico/analisi/spatial/spatial/K/results_seed/DEGs_endo_low_high.xlsx')
sc.tl.rank_genes_groups(adata_epi, 'level', groups=['LOW'], reference='HIGH', method='wilcoxon')
df_adata_epi = sc.get.rank_genes_groups_df(adata_epi, group='LOW',key = "rank_genes_groups")
df_adata_epi.to_excel('/Users/paolamaragno/Library/CloudStorage/OneDrive-UniversitàdegliStudidiMilano/Policlinico/analisi/spatial/spatial/K/results_seed/DEGs_epi_low_high.xlsx')
sc.tl.rank_genes_groups(adata_fibro, 'level', groups=['LOW'], reference='HIGH', method='wilcoxon')
df_adata_fibro = sc.get.rank_genes_groups_df(adata_fibro, group='LOW',key = "rank_genes_groups")
df_adata_fibro.to_excel('/Users/paolamaragno/Library/CloudStorage/OneDrive-UniversitàdegliStudidiMilano/Policlinico/analisi/spatial/spatial/K/results_seed/DEGs_fibro_low_high.xlsx')
sc.tl.rank_genes_groups(adata_goblet, 'level', groups=['LOW'], reference='HIGH', method='wilcoxon')
df_adata_goblet = sc.get.rank_genes_groups_df(adata_goblet, group='LOW',key = "rank_genes_groups")
df_adata_goblet.to_excel('/Users/paolamaragno/Library/CloudStorage/OneDrive-UniversitàdegliStudidiMilano/Policlinico/analisi/spatial/spatial/K/results_seed/DEGs_goblet_low_high.xlsx')
sc.tl.rank_genes_groups(adata_macro, 'level', groups=['LOW'], reference='HIGH', method='wilcoxon')
df_adata_macro = sc.get.rank_genes_groups_df(adata_macro, group='LOW',key = "rank_genes_groups")
df_adata_macro.to_excel('/Users/paolamaragno/Library/CloudStorage/OneDrive-UniversitàdegliStudidiMilano/Policlinico/analisi/spatial/spatial/K/results_seed/DEGs_macro_low_high.xlsx')
sc.tl.rank_genes_groups(adata_mast, 'level', groups=['LOW'], reference='HIGH', method='wilcoxon')
df_adata_mast = sc.get.rank_genes_groups_df(adata_mast, group='LOW',key = "rank_genes_groups")
df_adata_mast.to_excel('/Users/paolamaragno/Library/CloudStorage/OneDrive-UniversitàdegliStudidiMilano/Policlinico/analisi/spatial/spatial/K/results_seed/DEGs_mast_low_high.xlsx')
sc.tl.rank_genes_groups(adata_mono, 'level', groups=['LOW'], reference='HIGH', method='wilcoxon')
df_adata_mono = sc.get.rank_genes_groups_df(adata_mono, group='LOW',key = "rank_genes_groups")
df_adata_mono.to_excel('/Users/paolamaragno/Library/CloudStorage/OneDrive-UniversitàdegliStudidiMilano/Policlinico/analisi/spatial/spatial/K/results_seed/DEGs_mono_low_high.xlsx')
sc.tl.rank_genes_groups(adata_paneth, 'level', groups=['LOW'], reference='HIGH', method='wilcoxon')
df_adata_paneth = sc.get.rank_genes_groups_df(adata_paneth, group='LOW',key = "rank_genes_groups")
df_adata_paneth.to_excel('/Users/paolamaragno/Library/CloudStorage/OneDrive-UniversitàdegliStudidiMilano/Policlinico/analisi/spatial/spatial/K/results_seed/DEGs_paneth_low_high.xlsx')
sc.tl.rank_genes_groups(adata_peri, 'level', groups=['LOW'], reference='HIGH', method='wilcoxon')
df_adata_peri = sc.get.rank_genes_groups_df(adata_peri, group='LOW',key = "rank_genes_groups")
df_adata_peri.to_excel('/Users/paolamaragno/Library/CloudStorage/OneDrive-UniversitàdegliStudidiMilano/Policlinico/analisi/spatial/spatial/K/results_seed/DEGs_peri_low_high.xlsx')
sc.tl.rank_genes_groups(adata_plasma_A, 'level', groups=['LOW'], reference='HIGH', method='wilcoxon')
df_adata_plasma_A = sc.get.rank_genes_groups_df(adata_plasma_A, group='LOW',key = "rank_genes_groups")
df_adata_plasma_A.to_excel('/Users/paolamaragno/Library/CloudStorage/OneDrive-UniversitàdegliStudidiMilano/Policlinico/analisi/spatial/spatial/K/results_seed/DEGs_plasma_A_low_high.xlsx')
sc.tl.rank_genes_groups(adata_plasma_G, 'level', groups=['LOW'], reference='HIGH', method='wilcoxon')
df_adata_plasma_G = sc.get.rank_genes_groups_df(adata_plasma_G, group='LOW',key = "rank_genes_groups")
df_adata_plasma_G.to_excel('/Users/paolamaragno/Library/CloudStorage/OneDrive-UniversitàdegliStudidiMilano/Policlinico/analisi/spatial/spatial/K/results_seed/DEGs_plasma_G_low_high.xlsx')
sc.tl.rank_genes_groups(adata_muscle, 'level', groups=['LOW'], reference='HIGH', method='wilcoxon')
df_adata_muscle = sc.get.rank_genes_groups_df(adata_muscle, group='LOW',key = "rank_genes_groups")
df_adata_muscle.to_excel('/Users/paolamaragno/Library/CloudStorage/OneDrive-UniversitàdegliStudidiMilano/Policlinico/analisi/spatial/spatial/K/results_seed/DEGs_muscle_low_high.xlsx')
sc.tl.rank_genes_groups(adata_T, 'level', groups=['LOW'], reference='HIGH', method='wilcoxon')
df_adata_T = sc.get.rank_genes_groups_df(adata_T, group='LOW',key = "rank_genes_groups")
df_adata_T.to_excel('/Users/paolamaragno/Library/CloudStorage/OneDrive-UniversitàdegliStudidiMilano/Policlinico/analisi/spatial/spatial/K/results_seed/DEGs_T_low_high.xlsx')
/opt/anaconda3/envs/spatial_new/lib/python3.9/site-packages/scanpy/tools/_rank_genes_groups.py:645: ImplicitModificationWarning: Trying to modify attribute `._uns` of view, initializing view as actual.
adata.uns[key_added] = {}
/opt/anaconda3/envs/spatial_new/lib/python3.9/site-packages/scanpy/tools/_rank_genes_groups.py:645: ImplicitModificationWarning: Trying to modify attribute `._uns` of view, initializing view as actual.
adata.uns[key_added] = {}
/opt/anaconda3/envs/spatial_new/lib/python3.9/site-packages/scanpy/tools/_rank_genes_groups.py:645: ImplicitModificationWarning: Trying to modify attribute `._uns` of view, initializing view as actual.
adata.uns[key_added] = {}
/opt/anaconda3/envs/spatial_new/lib/python3.9/site-packages/scanpy/tools/_rank_genes_groups.py:645: ImplicitModificationWarning: Trying to modify attribute `._uns` of view, initializing view as actual.
adata.uns[key_added] = {}
/opt/anaconda3/envs/spatial_new/lib/python3.9/site-packages/scanpy/tools/_rank_genes_groups.py:645: ImplicitModificationWarning: Trying to modify attribute `._uns` of view, initializing view as actual.
adata.uns[key_added] = {}
/opt/anaconda3/envs/spatial_new/lib/python3.9/site-packages/scanpy/tools/_rank_genes_groups.py:645: ImplicitModificationWarning: Trying to modify attribute `._uns` of view, initializing view as actual.
adata.uns[key_added] = {}
/opt/anaconda3/envs/spatial_new/lib/python3.9/site-packages/scanpy/tools/_rank_genes_groups.py:645: ImplicitModificationWarning: Trying to modify attribute `._uns` of view, initializing view as actual.
adata.uns[key_added] = {}
/opt/anaconda3/envs/spatial_new/lib/python3.9/site-packages/scanpy/tools/_rank_genes_groups.py:645: ImplicitModificationWarning: Trying to modify attribute `._uns` of view, initializing view as actual.
adata.uns[key_added] = {}
/opt/anaconda3/envs/spatial_new/lib/python3.9/site-packages/scanpy/tools/_rank_genes_groups.py:645: ImplicitModificationWarning: Trying to modify attribute `._uns` of view, initializing view as actual.
adata.uns[key_added] = {}
/opt/anaconda3/envs/spatial_new/lib/python3.9/site-packages/scanpy/tools/_rank_genes_groups.py:645: ImplicitModificationWarning: Trying to modify attribute `._uns` of view, initializing view as actual.
adata.uns[key_added] = {}
/opt/anaconda3/envs/spatial_new/lib/python3.9/site-packages/scanpy/tools/_rank_genes_groups.py:645: ImplicitModificationWarning: Trying to modify attribute `._uns` of view, initializing view as actual.
adata.uns[key_added] = {}
/opt/anaconda3/envs/spatial_new/lib/python3.9/site-packages/scanpy/tools/_rank_genes_groups.py:645: ImplicitModificationWarning: Trying to modify attribute `._uns` of view, initializing view as actual.
adata.uns[key_added] = {}
/opt/anaconda3/envs/spatial_new/lib/python3.9/site-packages/scanpy/tools/_rank_genes_groups.py:645: ImplicitModificationWarning: Trying to modify attribute `._uns` of view, initializing view as actual.
adata.uns[key_added] = {}
/opt/anaconda3/envs/spatial_new/lib/python3.9/site-packages/scanpy/tools/_rank_genes_groups.py:645: ImplicitModificationWarning: Trying to modify attribute `._uns` of view, initializing view as actual.
adata.uns[key_added] = {}
/opt/anaconda3/envs/spatial_new/lib/python3.9/site-packages/scanpy/tools/_rank_genes_groups.py:645: ImplicitModificationWarning: Trying to modify attribute `._uns` of view, initializing view as actual.
adata.uns[key_added] = {}
/opt/anaconda3/envs/spatial_new/lib/python3.9/site-packages/scanpy/tools/_rank_genes_groups.py:645: ImplicitModificationWarning: Trying to modify attribute `._uns` of view, initializing view as actual.
adata.uns[key_added] = {}
In [25]:
a = adata_goblet[adata_goblet.obs.level =='HIGH'].obs.index
In [26]:
b = pd.DataFrame(adata_goblet.X.toarray(), index= adata_goblet.obs.index, columns = adata_goblet.var.index)
b.loc[a,'FN1'].mean()
Out[26]:
1.0489862833811534
In [27]:
a = adata_goblet[adata_goblet.obs.level =='LOW'].obs.index
b = pd.DataFrame(adata_goblet.X.toarray(), index= adata_goblet.obs.index, columns = adata_goblet.var.index)
b.loc[a,'FN1'].mean()
Out[27]:
0.05101307117387765
Identification of T cells with high expression of ZBTB16¶
Identification of epithelial, fibroblasts, CAF and CAF CXCL14+ within 500 micro from these iNKT cells
In [25]:
gene_expression_all_cells_T_cells = adata2[adata2.obs.leiden == 'T'].obs.index.values
gene_expression_all_cells = pd.DataFrame(adata2.X.toarray(),index = adata2.obs.index.values, columns = adata2.var.index.values)
gene_expression_T_cells= gene_expression_all_cells.loc[gene_expression_all_cells_T_cells]
gene_expression_T_cells
Out[25]:
| RAMP2 | CD83 | RYK | CD5L | NLRP2 | BIRC5 | LIF | H2AZ1 | PTGES3 | CDH11 | ... | PTEN | PXDN | RGS13 | SLA | SLCO2B1 | SQLE | SRSF2 | TGFBI | TPI1 | ZBTB16 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 3_1 | 0.0 | 0.000000 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.000000 | ... | 0.0 | 0.0 | 0.0 | 0.000000 | 0.0 | 0.0 | 0.0 | 0.0 | 0.926547 | 0.0 |
| 8_1 | 0.0 | 0.000000 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.000000 | ... | 0.0 | 0.0 | 0.0 | 0.000000 | 0.0 | 0.0 | 0.0 | 0.0 | 0.000000 | 0.0 |
| 58_1 | 0.0 | 0.000000 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.000000 | ... | 0.0 | 0.0 | 0.0 | 0.000000 | 0.0 | 0.0 | 0.0 | 0.0 | 0.000000 | 0.0 |
| 59_1 | 0.0 | 0.000000 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.000000 | ... | 0.0 | 0.0 | 0.0 | 0.000000 | 0.0 | 0.0 | 0.0 | 0.0 | 0.000000 | 0.0 |
| 67_1 | 0.0 | 0.000000 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.000000 | ... | 0.0 | 0.0 | 0.0 | 0.965604 | 0.0 | 0.0 | 0.0 | 0.0 | 0.000000 | 0.0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 1642_20 | 0.0 | 0.000000 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.000000 | ... | 0.0 | 0.0 | 0.0 | 0.000000 | 0.0 | 0.0 | 0.0 | 0.0 | 0.000000 | 0.0 |
| 1648_20 | 0.0 | 0.000000 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.000000 | ... | 0.0 | 0.0 | 0.0 | 0.000000 | 0.0 | 0.0 | 0.0 | 0.0 | 0.000000 | 0.0 |
| 1651_20 | 0.0 | 0.000000 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.000000 | ... | 0.0 | 0.0 | 0.0 | 0.000000 | 0.0 | 0.0 | 0.0 | 0.0 | 0.000000 | 0.0 |
| 1684_20 | 0.0 | 1.528175 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.528175 | ... | 0.0 | 0.0 | 0.0 | 0.000000 | 0.0 | 0.0 | 0.0 | 0.0 | 0.000000 | 0.0 |
| 1700_20 | 0.0 | 0.000000 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.000000 | ... | 0.0 | 0.0 | 0.0 | 0.000000 | 0.0 | 0.0 | 0.0 | 0.0 | 0.000000 | 0.0 |
1391 rows × 992 columns
In [59]:
gene_expression_T_cells['ZBTB16'][gene_expression_T_cells['ZBTB16'] > 0.1]
gene_expression_T_cells['ZBTB16'].max()
Out[59]:
2.2467703138876494
In [26]:
adata_low = adata2[adata2.obs.level == 'LOW']
adata_high = adata2[adata2.obs.level == 'HIGH']
In [51]:
# for each fov, compute the how many T cells express ZBTB16 > 0
fov_ZBTB16_low = {}
for fov in pd.unique(adata_low.obs.fov):
adata_fov = adata_low[adata_low.obs.fov == fov]
index_T_cells = adata_fov[adata_fov.obs.leiden == 'T'].obs.index.values
gene_expression_fov_all_cells = pd.DataFrame(adata_fov.X.toarray(),index = adata_fov.obs.index.values, columns = adata_fov.var.index.values)
gene_expression_fov_T = gene_expression_fov_all_cells.loc[index_T_cells]
T_cells_ZBTB16 = gene_expression_fov_T['ZBTB16'][gene_expression_fov_T['ZBTB16'] > 0].index.values
fov_ZBTB16_low[fov] = len(T_cells_ZBTB16)
In [27]:
fov_ZBTB16_low = {}
for fov in pd.unique(adata_low.obs.fov):
adata_fov = adata_low[adata_low.obs.fov == fov]
index_T_cells = adata_fov[adata_fov.obs.leiden == 'T'].obs.index.values
gene_expression_fov_all_cells = pd.DataFrame(adata_fov.X.toarray(),index = adata_fov.obs.index.values, columns = adata_fov.var.index.values)
gene_expression_fov_T = gene_expression_fov_all_cells.loc[index_T_cells]
T_cells_ZBTB16 = gene_expression_fov_T['ZBTB16'][gene_expression_fov_T['ZBTB16'] > 0].index.values
gene_expression_fov_T_ZBTB16 = gene_expression_fov_all_cells.loc[T_cells_ZBTB16]
T_cells_ZBTB16_AHR = gene_expression_fov_T_ZBTB16['AHR'][gene_expression_fov_T['AHR'] > 0].index.values
T_cells_AHR= gene_expression_fov_T['AHR'][gene_expression_fov_T['AHR'] > 0].index.values
fov_ZBTB16_low[fov] = [len(T_cells_ZBTB16), len(T_cells_AHR), len(T_cells_ZBTB16_AHR)]
In [28]:
fov_ZBTB16_low
Out[28]:
{'1': [0, 1, 0],
'9': [2, 4, 0],
'10': [0, 1, 0],
'11': [1, 0, 0],
'14': [3, 2, 0],
'16': [0, 2, 0],
'17': [4, 1, 0],
'18': [1, 1, 0],
'19': [0, 0, 0],
'20': [1, 0, 0]}
In [36]:
# for each fov, compute the how many T cells express ZBTB16 > 0.1 and how many of them express IL10 > 0.1
fov_ZBTB16_high = {}
for fov in pd.unique(adata_high.obs.fov):
adata_fov = adata_high[adata_high.obs.fov == fov]
index_T_cells = adata_fov[adata_fov.obs.leiden == 'T'].obs.index.values
gene_expression_fov_all_cells = pd.DataFrame(adata_fov.X.toarray(),index = adata_fov.obs.index.values, columns = adata_fov.var.index.values)
gene_expression_fov_T = gene_expression_fov_all_cells.loc[index_T_cells]
T_cells_ZBTB16 = gene_expression_fov_T['ZBTB16'][gene_expression_fov_T['ZBTB16'] > 0].index.values
gene_expression_fov_T_ZBTB16 = gene_expression_fov_all_cells.loc[T_cells_ZBTB16]
T_cells_ZBTB16_AHR = gene_expression_fov_T_ZBTB16['AHR'][gene_expression_fov_T['AHR'] > 0].index.values
T_cells_AHR = gene_expression_fov_T['AHR'][gene_expression_fov_T['AHR'] > 0].index.values
fov_ZBTB16_high[fov] = [len(T_cells_ZBTB16), len(T_cells_AHR),len(T_cells_ZBTB16_AHR)]
In [37]:
fov_ZBTB16_high
Out[37]:
{'4': [5, 6, 0],
'5': [4, 9, 0],
'6': [15, 10, 2],
'7': [1, 4, 0],
'8': [3, 3, 1],
'12': [5, 1, 0],
'13': [4, 1, 0]}
In [53]:
# for each fov identify the percentage of the epithelial/fibroblast/CAF/CAF CXCL14 cells closer that x micro from T cells ZBTB16 > 0
# and the percentage of the epithelial/fibroblast/CAF/CAF CXCL14 cells farther that x micro
def percentage_cells(level, distance):
epithelial_less_ZBTB16 = []
fibroblast_less_ZBTB16 = []
CAF_less_ZBTB16 = []
CAF_CXCL14_less_ZBTB16 = []
epithelial_more_ZBTB16 = []
fibroblast_more_ZBTB16 = []
CAF_more_ZBTB16 = []
CAF_CXCL14_more_ZBTB16 = []
if level == 'high':
adata = adata_high
else:
adata = adata_low
for fov in pd.unique(adata.obs.fov):
adata_fov = adata[adata.obs.fov == fov]
index_T_cells = adata_fov[adata_fov.obs.leiden == 'T'].obs.index.values
gene_expression_fov_all_cells = pd.DataFrame(adata_fov.X.toarray(),index = adata_fov.obs.index.values, columns = adata_fov.var.index.values)
gene_expression_fov_T = gene_expression_fov_all_cells.loc[index_T_cells]
T_cells_ZBTB16 = gene_expression_fov_T['ZBTB16'][gene_expression_fov_T['ZBTB16'] > 0].index.values
pos = adata_fov.obs[['CenterX_global_px','CenterY_global_px']]
dm = pd.DataFrame(distance_matrix(pos, pos), index = pos.index.values, columns = pos.index.values)
epithelial_less_ZBTB16_fov = []
fibroblast_less_ZBTB16_fov = []
CAF_less_ZBTB16_fov = []
CAF_CXCL14_less_ZBTB16_fov = []
for cell_ZBTB16 in T_cells_ZBTB16:
dm_cell = dm[cell_ZBTB16]
dm_cell_less = dm_cell[dm_cell <= distance].index.values
epithelial_less = adata_fov.obs.loc[dm_cell_less].leiden[adata_fov.obs.loc[dm_cell_less].leiden == 'Epithelial'].index.values
if len(epithelial_less) > 0:
epithelial_less_ZBTB16_fov.extend(epithelial_less)
fibroblast_less = adata_fov.obs.loc[dm_cell_less].leiden[adata_fov.obs.loc[dm_cell_less].leiden == 'Fibroblast'].index.values
if len(fibroblast_less) > 0:
fibroblast_less_ZBTB16_fov.extend(fibroblast_less)
CAF_less = adata_fov.obs.loc[dm_cell_less].leiden[adata_fov.obs.loc[dm_cell_less].leiden == 'CAF'].index.values
if len(CAF_less) > 0:
CAF_less_ZBTB16_fov.extend(CAF_less)
CAF_CXCL14_less = adata_fov.obs.loc[dm_cell_less].leiden[adata_fov.obs.loc[dm_cell_less].leiden == 'CAF CXCL14'].index.values
if len(CAF_CXCL14_less) > 0:
CAF_CXCL14_less_ZBTB16_fov.extend(CAF_CXCL14_less)
epithelial_more_ZBTB16_fov = []
fibroblast_more_ZBTB16_fov = []
CAF_more_ZBTB16_fov = []
CAF_CXCL14_more_ZBTB16_fov = []
index_epithelial_cells = adata_fov[adata_fov.obs.leiden == 'Epithelial'].obs.index.values
index_fibroblast_cells = adata_fov[adata_fov.obs.leiden == 'Fibroblast'].obs.index.values
index_CAF_cells = adata_fov[adata_fov.obs.leiden == 'CAF'].obs.index.values
index_CAF_CXCL14_cells = adata_fov[adata_fov.obs.leiden == 'CAF CXCL14'].obs.index.values
for c in index_epithelial_cells:
if c not in np.unique(epithelial_less_ZBTB16_fov):
epithelial_more_ZBTB16_fov.append(c)
for c in index_fibroblast_cells:
if c not in np.unique(fibroblast_less_ZBTB16_fov):
fibroblast_more_ZBTB16_fov.append(c)
for c in index_CAF_cells:
if c not in np.unique(CAF_less_ZBTB16_fov):
CAF_more_ZBTB16_fov.append(c)
for c in index_CAF_CXCL14_cells:
if c not in np.unique(CAF_CXCL14_less_ZBTB16_fov):
CAF_CXCL14_more_ZBTB16_fov.append(c)
if len(adata_fov[adata_fov.obs.leiden == 'Epithelial']) != 0:
epithelial_less_ZBTB16.append(len(np.unique(epithelial_less_ZBTB16_fov))*100/len(adata_fov[adata_fov.obs.leiden == 'Epithelial']))
epithelial_more_ZBTB16.append(len(np.unique(epithelial_more_ZBTB16_fov))*100/len(adata_fov[adata_fov.obs.leiden == 'Epithelial']))
if len(adata_fov[adata_fov.obs.leiden == 'Fibroblast']) != 0:
fibroblast_less_ZBTB16.append(len(np.unique(fibroblast_less_ZBTB16_fov))*100/len(adata_fov[adata_fov.obs.leiden == 'Fibroblast']))
fibroblast_more_ZBTB16.append(len(np.unique(fibroblast_more_ZBTB16_fov))*100/len(adata_fov[adata_fov.obs.leiden == 'Fibroblast']))
if len(adata_fov[adata_fov.obs.leiden == 'CAF']) != 0:
CAF_less_ZBTB16.append(len(np.unique(CAF_less_ZBTB16_fov))*100/len(adata_fov[adata_fov.obs.leiden == 'CAF']))
CAF_more_ZBTB16.append(len(np.unique(CAF_more_ZBTB16_fov))*100/len(adata_fov[adata_fov.obs.leiden == 'CAF']))
if len(adata_fov[adata_fov.obs.leiden == 'CAF CXCL14']) != 0:
CAF_CXCL14_less_ZBTB16.append(len(np.unique(CAF_CXCL14_less_ZBTB16_fov))*100/len(adata_fov[adata_fov.obs.leiden == 'CAF CXCL14']))
CAF_CXCL14_more_ZBTB16.append(len(np.unique(CAF_CXCL14_more_ZBTB16_fov))*100/len(adata_fov[adata_fov.obs.leiden == 'CAF CXCL14']))
with open("/Users/paolamaragno/Library/CloudStorage/OneDrive-UniversitàdegliStudidiMilano/Policlinico/analisi/spatial/spatial/K/results_seed/number_epithelial_less_" + level + '_' + str(distance) + "_T_ZBTB16", "w") as file:
for row in epithelial_less_ZBTB16:
s = "".join(map(str, str(row)))
file.write(s+'\n')
with open("/Users/paolamaragno/Library/CloudStorage/OneDrive-UniversitàdegliStudidiMilano/Policlinico/analisi/spatial/spatial/K/results_seed/number_fibroblast_less_" + level + '_' + str(distance) + "_T_ZBTB16", "w") as file:
for row in fibroblast_less_ZBTB16:
s = "".join(map(str, str(row)))
file.write(s+'\n')
with open("/Users/paolamaragno/Library/CloudStorage/OneDrive-UniversitàdegliStudidiMilano/Policlinico/analisi/spatial/spatial/K/results_seed/number_CAF_less_" + level + '_' + str(distance) + "_T_ZBTB16", "w") as file:
for row in CAF_less_ZBTB16:
s = "".join(map(str, str(row)))
file.write(s+'\n')
with open("/Users/paolamaragno/Library/CloudStorage/OneDrive-UniversitàdegliStudidiMilano/Policlinico/analisi/spatial/spatial/K/results_seed/number_CAF_CXCL14_less_" + level + '_' + str(distance) + "_T_ZBTB16", "w") as file:
for row in CAF_CXCL14_less_ZBTB16:
s = "".join(map(str, str(row)))
file.write(s+'\n')
with open("/Users/paolamaragno/Library/CloudStorage/OneDrive-UniversitàdegliStudidiMilano/Policlinico/analisi/spatial/spatial/K/results_seed/number_epithelial_more_" + level + '_' + str(distance) + "_T_ZBTB16", "w") as file:
for row in epithelial_more_ZBTB16:
s = "".join(map(str, str(row)))
file.write(s+'\n')
with open("/Users/paolamaragno/Library/CloudStorage/OneDrive-UniversitàdegliStudidiMilano/Policlinico/analisi/spatial/spatial/K/results_seed/number_fibroblast_more_" + level + '_' + str(distance) + "_T_ZBTB16", "w") as file:
for row in fibroblast_more_ZBTB16:
s = "".join(map(str, str(row)))
file.write(s+'\n')
with open("/Users/paolamaragno/Library/CloudStorage/OneDrive-UniversitàdegliStudidiMilano/Policlinico/analisi/spatial/spatial/K/results_seed/number_CAF_more_" + level + '_' + str(distance) + "_T_ZBTB16", "w") as file:
for row in CAF_more_ZBTB16:
s = "".join(map(str, str(row)))
file.write(s+'\n')
with open("/Users/paolamaragno/Library/CloudStorage/OneDrive-UniversitàdegliStudidiMilano/Policlinico/analisi/spatial/spatial/K/results_seed/number_CAF_CXCL14_more_" + level + '_' + str(distance) + "_T_ZBTB16", "w") as file:
for row in CAF_CXCL14_more_ZBTB16:
s = "".join(map(str, str(row)))
file.write(s+'\n')
In [54]:
percentage_cells('low', 500)
percentage_cells('high', 500)
percentage_cells('low', 100)
percentage_cells('high', 100)
percentage_cells('low', 1000)
percentage_cells('high', 1000)
/var/folders/xx/sgggrhp55xx_8s1thvzkgr600000gn/T/ipykernel_5208/1036390152.py:62: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison if c not in np.unique(epithelial_less_ZBTB16_fov): /var/folders/xx/sgggrhp55xx_8s1thvzkgr600000gn/T/ipykernel_5208/1036390152.py:66: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison if c not in np.unique(fibroblast_less_ZBTB16_fov): /var/folders/xx/sgggrhp55xx_8s1thvzkgr600000gn/T/ipykernel_5208/1036390152.py:70: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison if c not in np.unique(CAF_less_ZBTB16_fov): /var/folders/xx/sgggrhp55xx_8s1thvzkgr600000gn/T/ipykernel_5208/1036390152.py:74: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison if c not in np.unique(CAF_CXCL14_less_ZBTB16_fov): /var/folders/xx/sgggrhp55xx_8s1thvzkgr600000gn/T/ipykernel_5208/1036390152.py:74: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison if c not in np.unique(CAF_CXCL14_less_ZBTB16_fov): /var/folders/xx/sgggrhp55xx_8s1thvzkgr600000gn/T/ipykernel_5208/1036390152.py:62: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison if c not in np.unique(epithelial_less_ZBTB16_fov): /var/folders/xx/sgggrhp55xx_8s1thvzkgr600000gn/T/ipykernel_5208/1036390152.py:66: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison if c not in np.unique(fibroblast_less_ZBTB16_fov): /var/folders/xx/sgggrhp55xx_8s1thvzkgr600000gn/T/ipykernel_5208/1036390152.py:70: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison if c not in np.unique(CAF_less_ZBTB16_fov): /var/folders/xx/sgggrhp55xx_8s1thvzkgr600000gn/T/ipykernel_5208/1036390152.py:74: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison if c not in np.unique(CAF_CXCL14_less_ZBTB16_fov): /var/folders/xx/sgggrhp55xx_8s1thvzkgr600000gn/T/ipykernel_5208/1036390152.py:62: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison if c not in np.unique(epithelial_less_ZBTB16_fov): /var/folders/xx/sgggrhp55xx_8s1thvzkgr600000gn/T/ipykernel_5208/1036390152.py:74: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison if c not in np.unique(CAF_CXCL14_less_ZBTB16_fov): /var/folders/xx/sgggrhp55xx_8s1thvzkgr600000gn/T/ipykernel_5208/1036390152.py:62: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison if c not in np.unique(epithelial_less_ZBTB16_fov): /var/folders/xx/sgggrhp55xx_8s1thvzkgr600000gn/T/ipykernel_5208/1036390152.py:62: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison if c not in np.unique(epithelial_less_ZBTB16_fov): /var/folders/xx/sgggrhp55xx_8s1thvzkgr600000gn/T/ipykernel_5208/1036390152.py:66: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison if c not in np.unique(fibroblast_less_ZBTB16_fov): /var/folders/xx/sgggrhp55xx_8s1thvzkgr600000gn/T/ipykernel_5208/1036390152.py:70: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison if c not in np.unique(CAF_less_ZBTB16_fov): /var/folders/xx/sgggrhp55xx_8s1thvzkgr600000gn/T/ipykernel_5208/1036390152.py:74: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison if c not in np.unique(CAF_CXCL14_less_ZBTB16_fov): /var/folders/xx/sgggrhp55xx_8s1thvzkgr600000gn/T/ipykernel_5208/1036390152.py:62: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison if c not in np.unique(epithelial_less_ZBTB16_fov): /var/folders/xx/sgggrhp55xx_8s1thvzkgr600000gn/T/ipykernel_5208/1036390152.py:66: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison if c not in np.unique(fibroblast_less_ZBTB16_fov): /var/folders/xx/sgggrhp55xx_8s1thvzkgr600000gn/T/ipykernel_5208/1036390152.py:62: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison if c not in np.unique(epithelial_less_ZBTB16_fov): /var/folders/xx/sgggrhp55xx_8s1thvzkgr600000gn/T/ipykernel_5208/1036390152.py:66: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison if c not in np.unique(fibroblast_less_ZBTB16_fov): /var/folders/xx/sgggrhp55xx_8s1thvzkgr600000gn/T/ipykernel_5208/1036390152.py:70: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison if c not in np.unique(CAF_less_ZBTB16_fov): /var/folders/xx/sgggrhp55xx_8s1thvzkgr600000gn/T/ipykernel_5208/1036390152.py:74: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison if c not in np.unique(CAF_CXCL14_less_ZBTB16_fov): /var/folders/xx/sgggrhp55xx_8s1thvzkgr600000gn/T/ipykernel_5208/1036390152.py:62: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison if c not in np.unique(epithelial_less_ZBTB16_fov): /var/folders/xx/sgggrhp55xx_8s1thvzkgr600000gn/T/ipykernel_5208/1036390152.py:66: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison if c not in np.unique(fibroblast_less_ZBTB16_fov): /var/folders/xx/sgggrhp55xx_8s1thvzkgr600000gn/T/ipykernel_5208/1036390152.py:74: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison if c not in np.unique(CAF_CXCL14_less_ZBTB16_fov): /var/folders/xx/sgggrhp55xx_8s1thvzkgr600000gn/T/ipykernel_5208/1036390152.py:66: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison if c not in np.unique(fibroblast_less_ZBTB16_fov): /var/folders/xx/sgggrhp55xx_8s1thvzkgr600000gn/T/ipykernel_5208/1036390152.py:66: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison if c not in np.unique(fibroblast_less_ZBTB16_fov): /var/folders/xx/sgggrhp55xx_8s1thvzkgr600000gn/T/ipykernel_5208/1036390152.py:66: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison if c not in np.unique(fibroblast_less_ZBTB16_fov): /var/folders/xx/sgggrhp55xx_8s1thvzkgr600000gn/T/ipykernel_5208/1036390152.py:74: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison if c not in np.unique(CAF_CXCL14_less_ZBTB16_fov): /var/folders/xx/sgggrhp55xx_8s1thvzkgr600000gn/T/ipykernel_5208/1036390152.py:62: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison if c not in np.unique(epithelial_less_ZBTB16_fov): /var/folders/xx/sgggrhp55xx_8s1thvzkgr600000gn/T/ipykernel_5208/1036390152.py:66: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison if c not in np.unique(fibroblast_less_ZBTB16_fov): /var/folders/xx/sgggrhp55xx_8s1thvzkgr600000gn/T/ipykernel_5208/1036390152.py:70: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison if c not in np.unique(CAF_less_ZBTB16_fov): /var/folders/xx/sgggrhp55xx_8s1thvzkgr600000gn/T/ipykernel_5208/1036390152.py:74: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison if c not in np.unique(CAF_CXCL14_less_ZBTB16_fov): /var/folders/xx/sgggrhp55xx_8s1thvzkgr600000gn/T/ipykernel_5208/1036390152.py:66: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison if c not in np.unique(fibroblast_less_ZBTB16_fov): /var/folders/xx/sgggrhp55xx_8s1thvzkgr600000gn/T/ipykernel_5208/1036390152.py:70: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison if c not in np.unique(CAF_less_ZBTB16_fov): /var/folders/xx/sgggrhp55xx_8s1thvzkgr600000gn/T/ipykernel_5208/1036390152.py:74: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison if c not in np.unique(CAF_CXCL14_less_ZBTB16_fov): /var/folders/xx/sgggrhp55xx_8s1thvzkgr600000gn/T/ipykernel_5208/1036390152.py:62: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison if c not in np.unique(epithelial_less_ZBTB16_fov): /var/folders/xx/sgggrhp55xx_8s1thvzkgr600000gn/T/ipykernel_5208/1036390152.py:66: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison if c not in np.unique(fibroblast_less_ZBTB16_fov): /var/folders/xx/sgggrhp55xx_8s1thvzkgr600000gn/T/ipykernel_5208/1036390152.py:70: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison if c not in np.unique(CAF_less_ZBTB16_fov): /var/folders/xx/sgggrhp55xx_8s1thvzkgr600000gn/T/ipykernel_5208/1036390152.py:74: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison if c not in np.unique(CAF_CXCL14_less_ZBTB16_fov): /var/folders/xx/sgggrhp55xx_8s1thvzkgr600000gn/T/ipykernel_5208/1036390152.py:62: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison if c not in np.unique(epithelial_less_ZBTB16_fov): /var/folders/xx/sgggrhp55xx_8s1thvzkgr600000gn/T/ipykernel_5208/1036390152.py:66: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison if c not in np.unique(fibroblast_less_ZBTB16_fov): /var/folders/xx/sgggrhp55xx_8s1thvzkgr600000gn/T/ipykernel_5208/1036390152.py:74: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison if c not in np.unique(CAF_CXCL14_less_ZBTB16_fov): /var/folders/xx/sgggrhp55xx_8s1thvzkgr600000gn/T/ipykernel_5208/1036390152.py:62: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison if c not in np.unique(epithelial_less_ZBTB16_fov): /var/folders/xx/sgggrhp55xx_8s1thvzkgr600000gn/T/ipykernel_5208/1036390152.py:66: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison if c not in np.unique(fibroblast_less_ZBTB16_fov): /var/folders/xx/sgggrhp55xx_8s1thvzkgr600000gn/T/ipykernel_5208/1036390152.py:74: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison if c not in np.unique(CAF_CXCL14_less_ZBTB16_fov): /var/folders/xx/sgggrhp55xx_8s1thvzkgr600000gn/T/ipykernel_5208/1036390152.py:62: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison if c not in np.unique(epithelial_less_ZBTB16_fov): /var/folders/xx/sgggrhp55xx_8s1thvzkgr600000gn/T/ipykernel_5208/1036390152.py:66: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison if c not in np.unique(fibroblast_less_ZBTB16_fov): /var/folders/xx/sgggrhp55xx_8s1thvzkgr600000gn/T/ipykernel_5208/1036390152.py:70: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison if c not in np.unique(CAF_less_ZBTB16_fov): /var/folders/xx/sgggrhp55xx_8s1thvzkgr600000gn/T/ipykernel_5208/1036390152.py:74: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison if c not in np.unique(CAF_CXCL14_less_ZBTB16_fov): /var/folders/xx/sgggrhp55xx_8s1thvzkgr600000gn/T/ipykernel_5208/1036390152.py:62: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison if c not in np.unique(epithelial_less_ZBTB16_fov): /var/folders/xx/sgggrhp55xx_8s1thvzkgr600000gn/T/ipykernel_5208/1036390152.py:74: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison if c not in np.unique(CAF_CXCL14_less_ZBTB16_fov): /var/folders/xx/sgggrhp55xx_8s1thvzkgr600000gn/T/ipykernel_5208/1036390152.py:66: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison if c not in np.unique(fibroblast_less_ZBTB16_fov): /var/folders/xx/sgggrhp55xx_8s1thvzkgr600000gn/T/ipykernel_5208/1036390152.py:70: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison if c not in np.unique(CAF_less_ZBTB16_fov): /var/folders/xx/sgggrhp55xx_8s1thvzkgr600000gn/T/ipykernel_5208/1036390152.py:62: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison if c not in np.unique(epithelial_less_ZBTB16_fov): /var/folders/xx/sgggrhp55xx_8s1thvzkgr600000gn/T/ipykernel_5208/1036390152.py:66: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison if c not in np.unique(fibroblast_less_ZBTB16_fov): /var/folders/xx/sgggrhp55xx_8s1thvzkgr600000gn/T/ipykernel_5208/1036390152.py:70: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison if c not in np.unique(CAF_less_ZBTB16_fov): /var/folders/xx/sgggrhp55xx_8s1thvzkgr600000gn/T/ipykernel_5208/1036390152.py:74: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison if c not in np.unique(CAF_CXCL14_less_ZBTB16_fov): /var/folders/xx/sgggrhp55xx_8s1thvzkgr600000gn/T/ipykernel_5208/1036390152.py:62: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison if c not in np.unique(epithelial_less_ZBTB16_fov): /var/folders/xx/sgggrhp55xx_8s1thvzkgr600000gn/T/ipykernel_5208/1036390152.py:66: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison if c not in np.unique(fibroblast_less_ZBTB16_fov): /var/folders/xx/sgggrhp55xx_8s1thvzkgr600000gn/T/ipykernel_5208/1036390152.py:66: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison if c not in np.unique(fibroblast_less_ZBTB16_fov): /var/folders/xx/sgggrhp55xx_8s1thvzkgr600000gn/T/ipykernel_5208/1036390152.py:70: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison if c not in np.unique(CAF_less_ZBTB16_fov): /var/folders/xx/sgggrhp55xx_8s1thvzkgr600000gn/T/ipykernel_5208/1036390152.py:74: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison if c not in np.unique(CAF_CXCL14_less_ZBTB16_fov): /var/folders/xx/sgggrhp55xx_8s1thvzkgr600000gn/T/ipykernel_5208/1036390152.py:66: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison if c not in np.unique(fibroblast_less_ZBTB16_fov): /var/folders/xx/sgggrhp55xx_8s1thvzkgr600000gn/T/ipykernel_5208/1036390152.py:74: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison if c not in np.unique(CAF_CXCL14_less_ZBTB16_fov): /var/folders/xx/sgggrhp55xx_8s1thvzkgr600000gn/T/ipykernel_5208/1036390152.py:74: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison if c not in np.unique(CAF_CXCL14_less_ZBTB16_fov): /var/folders/xx/sgggrhp55xx_8s1thvzkgr600000gn/T/ipykernel_5208/1036390152.py:66: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison if c not in np.unique(fibroblast_less_ZBTB16_fov): /var/folders/xx/sgggrhp55xx_8s1thvzkgr600000gn/T/ipykernel_5208/1036390152.py:66: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison if c not in np.unique(fibroblast_less_ZBTB16_fov): /var/folders/xx/sgggrhp55xx_8s1thvzkgr600000gn/T/ipykernel_5208/1036390152.py:70: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison if c not in np.unique(CAF_less_ZBTB16_fov): /var/folders/xx/sgggrhp55xx_8s1thvzkgr600000gn/T/ipykernel_5208/1036390152.py:66: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison if c not in np.unique(fibroblast_less_ZBTB16_fov): /var/folders/xx/sgggrhp55xx_8s1thvzkgr600000gn/T/ipykernel_5208/1036390152.py:70: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison if c not in np.unique(CAF_less_ZBTB16_fov): /var/folders/xx/sgggrhp55xx_8s1thvzkgr600000gn/T/ipykernel_5208/1036390152.py:66: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison if c not in np.unique(fibroblast_less_ZBTB16_fov): /var/folders/xx/sgggrhp55xx_8s1thvzkgr600000gn/T/ipykernel_5208/1036390152.py:74: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison if c not in np.unique(CAF_CXCL14_less_ZBTB16_fov): /var/folders/xx/sgggrhp55xx_8s1thvzkgr600000gn/T/ipykernel_5208/1036390152.py:62: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison if c not in np.unique(epithelial_less_ZBTB16_fov): /var/folders/xx/sgggrhp55xx_8s1thvzkgr600000gn/T/ipykernel_5208/1036390152.py:66: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison if c not in np.unique(fibroblast_less_ZBTB16_fov): /var/folders/xx/sgggrhp55xx_8s1thvzkgr600000gn/T/ipykernel_5208/1036390152.py:70: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison if c not in np.unique(CAF_less_ZBTB16_fov): /var/folders/xx/sgggrhp55xx_8s1thvzkgr600000gn/T/ipykernel_5208/1036390152.py:74: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison if c not in np.unique(CAF_CXCL14_less_ZBTB16_fov): /var/folders/xx/sgggrhp55xx_8s1thvzkgr600000gn/T/ipykernel_5208/1036390152.py:74: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison if c not in np.unique(CAF_CXCL14_less_ZBTB16_fov): /var/folders/xx/sgggrhp55xx_8s1thvzkgr600000gn/T/ipykernel_5208/1036390152.py:62: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison if c not in np.unique(epithelial_less_ZBTB16_fov): /var/folders/xx/sgggrhp55xx_8s1thvzkgr600000gn/T/ipykernel_5208/1036390152.py:66: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison if c not in np.unique(fibroblast_less_ZBTB16_fov): /var/folders/xx/sgggrhp55xx_8s1thvzkgr600000gn/T/ipykernel_5208/1036390152.py:70: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison if c not in np.unique(CAF_less_ZBTB16_fov): /var/folders/xx/sgggrhp55xx_8s1thvzkgr600000gn/T/ipykernel_5208/1036390152.py:74: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison if c not in np.unique(CAF_CXCL14_less_ZBTB16_fov): /var/folders/xx/sgggrhp55xx_8s1thvzkgr600000gn/T/ipykernel_5208/1036390152.py:62: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison if c not in np.unique(epithelial_less_ZBTB16_fov): /var/folders/xx/sgggrhp55xx_8s1thvzkgr600000gn/T/ipykernel_5208/1036390152.py:74: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison if c not in np.unique(CAF_CXCL14_less_ZBTB16_fov): /var/folders/xx/sgggrhp55xx_8s1thvzkgr600000gn/T/ipykernel_5208/1036390152.py:62: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison if c not in np.unique(epithelial_less_ZBTB16_fov): /var/folders/xx/sgggrhp55xx_8s1thvzkgr600000gn/T/ipykernel_5208/1036390152.py:62: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison if c not in np.unique(epithelial_less_ZBTB16_fov): /var/folders/xx/sgggrhp55xx_8s1thvzkgr600000gn/T/ipykernel_5208/1036390152.py:66: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison if c not in np.unique(fibroblast_less_ZBTB16_fov): /var/folders/xx/sgggrhp55xx_8s1thvzkgr600000gn/T/ipykernel_5208/1036390152.py:70: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison if c not in np.unique(CAF_less_ZBTB16_fov): /var/folders/xx/sgggrhp55xx_8s1thvzkgr600000gn/T/ipykernel_5208/1036390152.py:74: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison if c not in np.unique(CAF_CXCL14_less_ZBTB16_fov): /var/folders/xx/sgggrhp55xx_8s1thvzkgr600000gn/T/ipykernel_5208/1036390152.py:62: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison if c not in np.unique(epithelial_less_ZBTB16_fov): /var/folders/xx/sgggrhp55xx_8s1thvzkgr600000gn/T/ipykernel_5208/1036390152.py:66: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison if c not in np.unique(fibroblast_less_ZBTB16_fov): /var/folders/xx/sgggrhp55xx_8s1thvzkgr600000gn/T/ipykernel_5208/1036390152.py:62: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison if c not in np.unique(epithelial_less_ZBTB16_fov): /var/folders/xx/sgggrhp55xx_8s1thvzkgr600000gn/T/ipykernel_5208/1036390152.py:66: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison if c not in np.unique(fibroblast_less_ZBTB16_fov): /var/folders/xx/sgggrhp55xx_8s1thvzkgr600000gn/T/ipykernel_5208/1036390152.py:70: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison if c not in np.unique(CAF_less_ZBTB16_fov): /var/folders/xx/sgggrhp55xx_8s1thvzkgr600000gn/T/ipykernel_5208/1036390152.py:74: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison if c not in np.unique(CAF_CXCL14_less_ZBTB16_fov): /var/folders/xx/sgggrhp55xx_8s1thvzkgr600000gn/T/ipykernel_5208/1036390152.py:66: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison if c not in np.unique(fibroblast_less_ZBTB16_fov): /var/folders/xx/sgggrhp55xx_8s1thvzkgr600000gn/T/ipykernel_5208/1036390152.py:66: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison if c not in np.unique(fibroblast_less_ZBTB16_fov): /var/folders/xx/sgggrhp55xx_8s1thvzkgr600000gn/T/ipykernel_5208/1036390152.py:74: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison if c not in np.unique(CAF_CXCL14_less_ZBTB16_fov):
In [55]:
# for each fov identify the id of the epithelial/fibroblast/CAF/CAF CXCL14 cells closer that x nm from T cells ZBTB16 > 0
# and the id of the epithelial/fibroblast/CAF/CAF CXCL14 cells farther that x nm
def id_cells_within(level, distance):
epithelial_less_ZBTB16 = []
fibroblast_less_ZBTB16 = []
CAF_less_ZBTB16 = []
CAF_CXCL14_less_ZBTB16 = []
if level == 'high':
adata = adata_high
else:
adata = adata_low
for fov in pd.unique(adata.obs.fov):
adata_fov = adata[adata.obs.fov == fov]
index_T_cells = adata_fov[adata_fov.obs.leiden == 'T'].obs.index.values
gene_expression_fov_all_cells = pd.DataFrame(adata_fov.X.toarray(),index = adata_fov.obs.index.values, columns = adata_fov.var.index.values)
gene_expression_fov_T = gene_expression_fov_all_cells.loc[index_T_cells]
T_cells_ZBTB16 = gene_expression_fov_T['ZBTB16'][gene_expression_fov_T['ZBTB16'] > 0].index.values
pos = adata_fov.obs[['CenterX_global_px','CenterY_global_px']]
dm = pd.DataFrame(distance_matrix(pos, pos), index = pos.index.values, columns = pos.index.values)
for cell_ZBTB16 in T_cells_ZBTB16:
adata2.obs.loc[cell_ZBTB16,'within_'+str(distance)] = 'T ZBTB16+'
dm_cell = dm[cell_ZBTB16]
dm_cell_less = dm_cell[dm_cell <= distance].index.values
epithelial_less = adata_fov.obs.loc[dm_cell_less].leiden[adata_fov.obs.loc[dm_cell_less].leiden == 'Epithelial'].index.values
if len(epithelial_less) > 0:
epithelial_less_ZBTB16.extend(epithelial_less)
fibroblast_less = adata_fov.obs.loc[dm_cell_less].leiden[adata_fov.obs.loc[dm_cell_less].leiden == 'Fibroblast'].index.values
if len(fibroblast_less) > 0:
fibroblast_less_ZBTB16.extend(fibroblast_less)
CAF_less = adata_fov.obs.loc[dm_cell_less].leiden[adata_fov.obs.loc[dm_cell_less].leiden == 'CAF'].index.values
if len(CAF_less) > 0:
CAF_less_ZBTB16.extend(CAF_less)
CAF_CXCL14_less = adata_fov.obs.loc[dm_cell_less].leiden[adata_fov.obs.loc[dm_cell_less].leiden == 'CAF CXCL14'].index.values
if len(CAF_CXCL14_less) > 0:
CAF_CXCL14_less_ZBTB16.extend(CAF_CXCL14_less)
for c in epithelial_less_ZBTB16:
adata2.obs.loc[c,'within_'+str(distance)] = 'epithelial_within_'+str(distance)+'_ZBTB16'
adata2.obs.loc[c,'epithelial_'+str(distance) +'_ZBTB16'] = 'within'
for c in fibroblast_less_ZBTB16:
adata2.obs.loc[c,'within_'+str(distance)] = 'fibroblast_within_'+str(distance)+'_ZBTB16'
adata2.obs.loc[c,'fibroblast_'+str(distance) +'_ZBTB16'] = 'within'
for c in CAF_less_ZBTB16:
adata2.obs.loc[c,'within_'+str(distance)] = 'CAF_within_'+str(distance)+'_ZBTB16'
adata2.obs.loc[c,'CAF_'+str(distance) +'_ZBTB16'] = 'within'
for c in CAF_CXCL14_less_ZBTB16:
adata2.obs.loc[c,'within_'+str(distance)] = 'CAF CXCL14_within_'+str(distance)+'_ZBTB16'
adata2.obs.loc[c,'CAF CXCL14_'+str(distance) +'_ZBTB16'] = 'within'
with open("/Users/paolamaragno/Library/CloudStorage/OneDrive-UniversitàdegliStudidiMilano/Policlinico/analisi/spatial/spatial/K/results_seed/epithelial_less_" + level + '_' + str(distance) + "_T_ZBTB16", "w") as file:
for row in np.unique(epithelial_less_ZBTB16):
s = "".join(map(str, row))
file.write(s+'\n')
with open("/Users/paolamaragno/Library/CloudStorage/OneDrive-UniversitàdegliStudidiMilano/Policlinico/analisi/spatial/spatial/K/results_seed/fibroblast_less_" + level + '_' + str(distance) + "_T_ZBTB16", "w") as file:
for row in np.unique(fibroblast_less_ZBTB16):
s = "".join(map(str, row))
file.write(s+'\n')
with open("/Users/paolamaragno/Library/CloudStorage/OneDrive-UniversitàdegliStudidiMilano/Policlinico/analisi/spatial/spatial/K/results_seed/CAF_less_" + level + '_' + str(distance) + "_T_ZBTB16", "w") as file:
for row in np.unique(CAF_less_ZBTB16):
s = "".join(map(str, row))
file.write(s+'\n')
with open("/Users/paolamaragno/Library/CloudStorage/OneDrive-UniversitàdegliStudidiMilano/Policlinico/analisi/spatial/spatial/K/results_seed/CAF_CXCL14_less_" + level + '_' + str(distance) + "_T_ZBTB16", "w") as file:
for row in np.unique(CAF_CXCL14_less_ZBTB16):
s = "".join(map(str, row))
file.write(s+'\n')
epithelial_more_ZBTB16 = []
fibroblast_more_ZBTB16 = []
CAF_more_ZBTB16 = []
CAF_CXCL14_more_ZBTB16 = []
for fov in pd.unique(adata.obs.fov):
adata_fov = adata[adata.obs.fov == fov]
index_epithelial_cells = adata_fov[adata_fov.obs.leiden == 'Epithelial'].obs.index.values
index_fibroblast_cells = adata_fov[adata_fov.obs.leiden == 'Fibroblast'].obs.index.values
index_CAF_cells = adata_fov[adata_fov.obs.leiden == 'CAF'].obs.index.values
index_CAF_CXCL14_cells = adata_fov[adata_fov.obs.leiden == 'CAF CXCL14'].obs.index.values
for c in index_epithelial_cells:
if c not in np.unique(epithelial_less_ZBTB16):
epithelial_more_ZBTB16.append(c)
for c in index_fibroblast_cells:
if c not in np.unique(fibroblast_less_ZBTB16):
fibroblast_more_ZBTB16.append(c)
for c in index_CAF_cells:
if c not in np.unique(CAF_less_ZBTB16):
CAF_more_ZBTB16.append(c)
for c in index_CAF_CXCL14_cells:
if c not in np.unique(CAF_CXCL14_less_ZBTB16):
CAF_CXCL14_more_ZBTB16.append(c)
with open("/Users/paolamaragno/Library/CloudStorage/OneDrive-UniversitàdegliStudidiMilano/Policlinico/analisi/spatial/spatial/K/results_seed/epithelial_more_" + level + '_' + str(distance) + "_T_ZBTB16", "w") as file:
for row in epithelial_more_ZBTB16:
s = "".join(map(str, row))
file.write(s+'\n')
with open("/Users/paolamaragno/Library/CloudStorage/OneDrive-UniversitàdegliStudidiMilano/Policlinico/analisi/spatial/spatial/K/results_seed/fibroblast_more_" + level + '_' + str(distance) + "_T_ZBTB16", "w") as file:
for row in fibroblast_more_ZBTB16:
s = "".join(map(str, row))
file.write(s+'\n')
with open("/Users/paolamaragno/Library/CloudStorage/OneDrive-UniversitàdegliStudidiMilano/Policlinico/analisi/spatial/spatial/K/results_seed/CAF_more_" + level + '_' + str(distance) + "_T_ZBTB16", "w") as file:
for row in CAF_more_ZBTB16:
s = "".join(map(str, row))
file.write(s+'\n')
with open("/Users/paolamaragno/Library/CloudStorage/OneDrive-UniversitàdegliStudidiMilano/Policlinico/analisi/spatial/spatial/K/results_seed/CAF_CXCL14_more_" + level + '_' + str(distance) + "_T_ZBTB16", "w") as file:
for row in CAF_CXCL14_more_ZBTB16:
s = "".join(map(str, row))
file.write(s+'\n')
for c in epithelial_more_ZBTB16:
adata2.obs.loc[c,'epithelial_'+str(distance) +'_ZBTB16'] = 'farther'
for c in fibroblast_more_ZBTB16:
adata2.obs.loc[c,'fibroblast_'+str(distance) +'_ZBTB16'] = 'farther'
for c in CAF_more_ZBTB16:
adata2.obs.loc[c,'CAF_'+str(distance) +'_ZBTB16'] = 'farther'
for c in CAF_CXCL14_more_ZBTB16:
adata2.obs.loc[c,'CAF CXCL14_'+str(distance) +'_ZBTB16'] = 'farther'
In [56]:
id_cells_within('high', 100)
id_cells_within('low', 100)
id_cells_within('high', 500)
id_cells_within('low', 500)
id_cells_within('high', 1000)
id_cells_within('low', 1000)
/var/folders/xx/sgggrhp55xx_8s1thvzkgr600000gn/T/ipykernel_5208/1048384199.py:104: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison if c not in np.unique(CAF_CXCL14_less_ZBTB16):
In [57]:
adata_sub_100 = adata2[adata2.obs['within_100'].notna()]
adata_sub_500 = adata2[adata2.obs['within_500'].notna()]
adata_sub_1000 = adata2[adata2.obs['within_1000'].notna()]
In [20]:
np.array(pd.unique(adata_sub_100.obs['fov']))
Out[20]:
array(['4', '5', '6', '7', '8', '9', '11', '12', '13', '14', '17', '18',
'20'], dtype=object)
In [21]:
t_100 = ['fov 11 - Patient_6 - LOW','fov 12 - Patient_7 - HIGH',
'fov 13 - Patient_7 - HIGH',
'fov 14 - Patient_8 - LOW',
'fov 17 - Patient_8 - LOW',
'fov 18 - Patient_9 - LOW',
'fov 20 - Patient_9 - LOW',
'fov 4 - Patient_2 - HIGH',
'fov 5 - Patient_2 - HIGH',
'fov 6 - Patient_4 - HIGH',
'fov 7 - Patient_4 - HIGH',
'fov 8 - Patient_4 - HIGH',
'fov 9 - Patient_6 - LOW']
In [22]:
np.array(pd.unique(adata_sub_500.obs['fov']))
Out[22]:
array(['4', '5', '6', '7', '8', '9', '11', '12', '13', '14', '17', '18',
'20'], dtype=object)
In [23]:
t_500 = ['fov 11 - Patient_6 - LOW','fov 12 - Patient_7 - HIGH',
'fov 13 - Patient_7 - HIGH',
'fov 14 - Patient_8 - LOW',
'fov 17 - Patient_8 - LOW',
'fov 18 - Patient_9 - LOW',
'fov 20 - Patient_9 - LOW',
'fov 4 - Patient_2 - HIGH',
'fov 5 - Patient_2 - HIGH',
'fov 6 - Patient_4 - HIGH',
'fov 7 - Patient_4 - HIGH',
'fov 8 - Patient_4 - HIGH',
'fov 9 - Patient_6 - LOW']
In [24]:
np.array(pd.unique(adata_sub_1000.obs['fov']))
Out[24]:
array(['4', '5', '6', '7', '8', '9', '11', '12', '13', '14', '17', '18',
'20'], dtype=object)
In [25]:
t_1000 = ['fov 11 - Patient_6 - LOW','fov 12 - Patient_7 - HIGH',
'fov 13 - Patient_7 - HIGH',
'fov 14 - Patient_8 - LOW',
'fov 17 - Patient_8 - LOW',
'fov 18 - Patient_9 - LOW',
'fov 20 - Patient_9 - LOW',
'fov 4 - Patient_2 - HIGH',
'fov 5 - Patient_2 - HIGH',
'fov 6 - Patient_4 - HIGH',
'fov 7 - Patient_4 - HIGH',
'fov 8 - Patient_4 - HIGH',
'fov 9 - Patient_6 - LOW']
In [26]:
pd.unique(adata_sub_100.obs['within_100'])
Out[26]:
array(['T ZBTB16+', 'CAF_within_100_ZBTB16',
'epithelial_within_100_ZBTB16', 'fibroblast_within_100_ZBTB16',
'CAF CXCL14_within_100_ZBTB16'], dtype=object)
In [27]:
palette = ['#F0E442','#000000', '#56B4E9', '#D55E00', '#0072B2' ]
sns.color_palette(palette)
Out[27]:
In [28]:
sc.pl.umap(
adata_sub_100, show=False,
color= "within_100", palette=sns.color_palette(palette, 5)
)
/opt/anaconda3/envs/spatial_new/lib/python3.9/site-packages/anndata/_core/anndata.py:1145: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual. df[key] = c /opt/anaconda3/envs/spatial_new/lib/python3.9/site-packages/anndata/_core/anndata.py:1145: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual. df[key] = c /opt/anaconda3/envs/spatial_new/lib/python3.9/site-packages/anndata/_core/anndata.py:1145: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual. df[key] = c /opt/anaconda3/envs/spatial_new/lib/python3.9/site-packages/anndata/_core/anndata.py:1145: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual. df[key] = c /opt/anaconda3/envs/spatial_new/lib/python3.9/site-packages/anndata/_core/anndata.py:1145: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual. df[key] = c /opt/anaconda3/envs/spatial_new/lib/python3.9/site-packages/anndata/_core/anndata.py:1145: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual. df[key] = c /opt/anaconda3/envs/spatial_new/lib/python3.9/site-packages/anndata/_core/anndata.py:1145: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual. df[key] = c /opt/anaconda3/envs/spatial_new/lib/python3.9/site-packages/anndata/_core/anndata.py:1145: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual. df[key] = c /opt/anaconda3/envs/spatial_new/lib/python3.9/site-packages/anndata/_core/anndata.py:1145: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual. df[key] = c /opt/anaconda3/envs/spatial_new/lib/python3.9/site-packages/anndata/_core/anndata.py:1145: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual. df[key] = c /opt/anaconda3/envs/spatial_new/lib/python3.9/site-packages/anndata/_core/anndata.py:1145: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual. df[key] = c /opt/anaconda3/envs/spatial_new/lib/python3.9/site-packages/anndata/_core/anndata.py:1145: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual. df[key] = c /opt/anaconda3/envs/spatial_new/lib/python3.9/site-packages/anndata/_core/anndata.py:1145: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual. df[key] = c /opt/anaconda3/envs/spatial_new/lib/python3.9/site-packages/anndata/_core/anndata.py:1145: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual. df[key] = c /opt/anaconda3/envs/spatial_new/lib/python3.9/site-packages/anndata/_core/anndata.py:1145: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual. df[key] = c
Out[28]:
<Axes: title={'center': 'within_100'}, xlabel='UMAP1', ylabel='UMAP2'>
In [29]:
sq.pl.spatial_segment(
adata_sub_100,
color='within_100',
library_key="fov",
seg_cell_id="cell_ID",
seg_outline=True,
img=False,
scalebar_dx=1.0,
title = t_100,
scalebar_kwargs={"scale_loc": "bottom", "location": "lower right"}
)
plt.savefig('/Users/paolamaragno/Library/CloudStorage/OneDrive-UniversitàdegliStudidiMilano/Policlinico/analisi/spatial/spatial/K/results_seed/fov_patients_within100_senza_back.pdf',bbox_inches="tight")
In [30]:
pd.unique(adata_sub_500.obs['within_500'])
Out[30]:
array(['CAF_within_500_ZBTB16', 'CAF CXCL14_within_500_ZBTB16',
'fibroblast_within_500_ZBTB16', 'T ZBTB16+',
'epithelial_within_500_ZBTB16'], dtype=object)
In [31]:
sc.pl.umap(
adata_sub_500, show=False,
color= "within_500", palette=sns.color_palette(palette, 5)
)
/opt/anaconda3/envs/spatial_new/lib/python3.9/site-packages/anndata/_core/anndata.py:1145: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual. df[key] = c /opt/anaconda3/envs/spatial_new/lib/python3.9/site-packages/anndata/_core/anndata.py:1145: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual. df[key] = c /opt/anaconda3/envs/spatial_new/lib/python3.9/site-packages/anndata/_core/anndata.py:1145: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual. df[key] = c /opt/anaconda3/envs/spatial_new/lib/python3.9/site-packages/anndata/_core/anndata.py:1145: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual. df[key] = c /opt/anaconda3/envs/spatial_new/lib/python3.9/site-packages/anndata/_core/anndata.py:1145: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual. df[key] = c /opt/anaconda3/envs/spatial_new/lib/python3.9/site-packages/anndata/_core/anndata.py:1145: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual. df[key] = c /opt/anaconda3/envs/spatial_new/lib/python3.9/site-packages/anndata/_core/anndata.py:1145: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual. df[key] = c /opt/anaconda3/envs/spatial_new/lib/python3.9/site-packages/anndata/_core/anndata.py:1145: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual. df[key] = c /opt/anaconda3/envs/spatial_new/lib/python3.9/site-packages/anndata/_core/anndata.py:1145: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual. df[key] = c /opt/anaconda3/envs/spatial_new/lib/python3.9/site-packages/anndata/_core/anndata.py:1145: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual. df[key] = c /opt/anaconda3/envs/spatial_new/lib/python3.9/site-packages/anndata/_core/anndata.py:1145: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual. df[key] = c /opt/anaconda3/envs/spatial_new/lib/python3.9/site-packages/anndata/_core/anndata.py:1145: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual. df[key] = c /opt/anaconda3/envs/spatial_new/lib/python3.9/site-packages/anndata/_core/anndata.py:1145: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual. df[key] = c /opt/anaconda3/envs/spatial_new/lib/python3.9/site-packages/anndata/_core/anndata.py:1145: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual. df[key] = c /opt/anaconda3/envs/spatial_new/lib/python3.9/site-packages/anndata/_core/anndata.py:1145: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual. df[key] = c
Out[31]:
<Axes: title={'center': 'within_500'}, xlabel='UMAP1', ylabel='UMAP2'>
In [32]:
sq.pl.spatial_segment(
adata_sub_500,
color='within_500',
library_key="fov",
seg_cell_id="cell_ID",
seg_outline=True,
img=False,
scalebar_dx=1.0,
title = t_500,
scalebar_kwargs={"scale_loc": "bottom", "location": "lower right"}
)
plt.savefig('/Users/paolamaragno/Library/CloudStorage/OneDrive-UniversitàdegliStudidiMilano/Policlinico/analisi/spatial/spatial/K/results_seed/fov_patients_within500_senza_back.pdf',bbox_inches="tight")
In [33]:
pd.unique(adata_sub_1000.obs['within_1000'])
Out[33]:
array(['fibroblast_within_1000_ZBTB16', 'CAF_within_1000_ZBTB16',
'CAF CXCL14_within_1000_ZBTB16', 'T ZBTB16+',
'epithelial_within_1000_ZBTB16'], dtype=object)
In [34]:
sc.pl.umap(
adata_sub_1000, show=False,
color= "within_1000", palette=sns.color_palette(palette, 5)
)
/opt/anaconda3/envs/spatial_new/lib/python3.9/site-packages/anndata/_core/anndata.py:1145: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual. df[key] = c /opt/anaconda3/envs/spatial_new/lib/python3.9/site-packages/anndata/_core/anndata.py:1145: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual. df[key] = c /opt/anaconda3/envs/spatial_new/lib/python3.9/site-packages/anndata/_core/anndata.py:1145: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual. df[key] = c /opt/anaconda3/envs/spatial_new/lib/python3.9/site-packages/anndata/_core/anndata.py:1145: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual. df[key] = c /opt/anaconda3/envs/spatial_new/lib/python3.9/site-packages/anndata/_core/anndata.py:1145: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual. df[key] = c /opt/anaconda3/envs/spatial_new/lib/python3.9/site-packages/anndata/_core/anndata.py:1145: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual. df[key] = c /opt/anaconda3/envs/spatial_new/lib/python3.9/site-packages/anndata/_core/anndata.py:1145: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual. df[key] = c /opt/anaconda3/envs/spatial_new/lib/python3.9/site-packages/anndata/_core/anndata.py:1145: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual. df[key] = c /opt/anaconda3/envs/spatial_new/lib/python3.9/site-packages/anndata/_core/anndata.py:1145: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual. df[key] = c /opt/anaconda3/envs/spatial_new/lib/python3.9/site-packages/anndata/_core/anndata.py:1145: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual. df[key] = c /opt/anaconda3/envs/spatial_new/lib/python3.9/site-packages/anndata/_core/anndata.py:1145: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual. df[key] = c /opt/anaconda3/envs/spatial_new/lib/python3.9/site-packages/anndata/_core/anndata.py:1145: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual. df[key] = c /opt/anaconda3/envs/spatial_new/lib/python3.9/site-packages/anndata/_core/anndata.py:1145: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual. df[key] = c /opt/anaconda3/envs/spatial_new/lib/python3.9/site-packages/anndata/_core/anndata.py:1145: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual. df[key] = c /opt/anaconda3/envs/spatial_new/lib/python3.9/site-packages/anndata/_core/anndata.py:1145: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual. df[key] = c
Out[34]:
<Axes: title={'center': 'within_1000'}, xlabel='UMAP1', ylabel='UMAP2'>
In [35]:
sq.pl.spatial_segment(
adata_sub_1000,
color='within_1000',
library_key="fov",
seg_cell_id="cell_ID",
seg_outline=True,
img=False,
scalebar_dx=1.0,
title = t_1000,
scalebar_kwargs={"scale_loc": "bottom", "location": "lower right"}
)
plt.savefig('/Users/paolamaragno/Library/CloudStorage/OneDrive-UniversitàdegliStudidiMilano/Policlinico/analisi/spatial/spatial/K/results_seed/fov_patients_within1000_senza_back.pdf',bbox_inches="tight")
DEGs between epithelial cells/fibro/CAF/CAF CXCL14 within/farther than 100/500/10000 micro from T ZBTB16¶
In [25]:
adata_low = adata2[adata2.obs.level == 'LOW']
adata_high = adata2[adata2.obs.level == 'HIGH']
In [39]:
sc.tl.rank_genes_groups(adata_low, 'epithelial_500_ZBTB16', groups=['within'], reference='farther', method='wilcoxon')
df_adata_epi_low_500 = sc.get.rank_genes_groups_df(adata_low, group='within',key = "rank_genes_groups")
df_adata_epi_low_500.to_excel('/Users/paolamaragno/Library/CloudStorage/OneDrive-UniversitàdegliStudidiMilano/Policlinico/analisi/spatial/spatial/K/results_seed/DEGs_epithelial_within_farther_500_ZBTB16_low.xlsx')
sc.tl.rank_genes_groups(adata_high, 'epithelial_500_ZBTB16', groups=['within'], reference='farther', method='wilcoxon')
df_adata_epi_high_500 = sc.get.rank_genes_groups_df(adata_high, group='within',key = "rank_genes_groups")
df_adata_epi_high_500.to_excel('/Users/paolamaragno/Library/CloudStorage/OneDrive-UniversitàdegliStudidiMilano/Policlinico/analisi/spatial/spatial/K/results_seed/DEGs_epithelial_within_farther_500_ZBTB16_high.xlsx')
sc.tl.rank_genes_groups(adata_low, 'fibroblast_500_ZBTB16', groups=['within'], reference='farther', method='wilcoxon')
df_adata_fibro_low_500 = sc.get.rank_genes_groups_df(adata_low, group='within',key = "rank_genes_groups")
df_adata_fibro_low_500.to_excel('/Users/paolamaragno/Library/CloudStorage/OneDrive-UniversitàdegliStudidiMilano/Policlinico/analisi/spatial/spatial/K/results_seed/DEGs_fibro_within_farther_500_ZBTB16_low.xlsx')
sc.tl.rank_genes_groups(adata_high, 'fibroblast_500_ZBTB16', groups=['within'], reference='farther', method='wilcoxon')
df_adata_fibro_high_500 = sc.get.rank_genes_groups_df(adata_high, group='within',key = "rank_genes_groups")
df_adata_fibro_high_500.to_excel('/Users/paolamaragno/Library/CloudStorage/OneDrive-UniversitàdegliStudidiMilano/Policlinico/analisi/spatial/spatial/K/results_seed/DEGs_fibro_within_farther_500_ZBTB16_high.xlsx')
sc.tl.rank_genes_groups(adata_low, 'CAF_500_ZBTB16', groups=['within'], reference='farther', method='wilcoxon')
df_adata_CAF_low_500 = sc.get.rank_genes_groups_df(adata_low, group='within',key = "rank_genes_groups")
df_adata_CAF_low_500.to_excel('/Users/paolamaragno/Library/CloudStorage/OneDrive-UniversitàdegliStudidiMilano/Policlinico/analisi/spatial/spatial/K/results_seed/DEGs_CAF_within_farther_500_ZBTB16_low.xlsx')
sc.tl.rank_genes_groups(adata_high, 'CAF_500_ZBTB16', groups=['within'], reference='farther', method='wilcoxon')
df_adata_CAF_high_500 = sc.get.rank_genes_groups_df(adata_high, group='within',key = "rank_genes_groups")
df_adata_CAF_high_500.to_excel('/Users/paolamaragno/Library/CloudStorage/OneDrive-UniversitàdegliStudidiMilano/Policlinico/analisi/spatial/spatial/K/results_seed/DEGs_CAF_within_farther_500_ZBTB16_high.xlsx')
sc.tl.rank_genes_groups(adata_low, 'CAF CXCL14_500_ZBTB16', groups=['within'], reference='farther', method='wilcoxon')
df_adata_CAF_CXCL14_low_500 = sc.get.rank_genes_groups_df(adata_low, group='within',key = "rank_genes_groups")
df_adata_CAF_CXCL14_low_500.to_excel('/Users/paolamaragno/Library/CloudStorage/OneDrive-UniversitàdegliStudidiMilano/Policlinico/analisi/spatial/spatial/K/results_seed/DEGs_CAF_CXCL14_within_farther_500_ZBTB16_low.xlsx')
sc.tl.rank_genes_groups(adata_high, 'CAF CXCL14_500_ZBTB16', groups=['within'], reference='farther', method='wilcoxon')
df_adata_CAF_CXCL14_high_500 = sc.get.rank_genes_groups_df(adata_high, group='within',key = "rank_genes_groups")
df_adata_CAF_CXCL14_high_500.to_excel('/Users/paolamaragno/Library/CloudStorage/OneDrive-UniversitàdegliStudidiMilano/Policlinico/analisi/spatial/spatial/K/results_seed/DEGs_CAF_CXCL14_within_farther_500_ZBTB16_high.xlsx')
In [41]:
a = adata_high[adata_high.obs['epithelial_500_ZBTB16'] == 'within'].obs.index
a
Out[41]:
Index(['362_5', '368_5', '369_5', '370_5', '377_5', '389_5', '390_5', '392_5',
'397_5', '404_5', '405_5', '412_5', '415_5', '419_5', '420_5', '421_5',
'423_5', '424_5', '443_5', '449_5', '450_5', '459_5', '461_5', '468_5',
'486_5', '496_5', '498_5', '507_5', '511_5', '516_5', '517_5', '524_5',
'540_5', '551_5', '564_5', '1541_5', '1562_5', '1573_5', '1598_5',
'1610_5', '1622_5', '1632_5', '1667_5', '1725_5', '1732_5', '1745_5',
'1757_5', '1761_5', '1792_5', '1823_5', '1824_5', '1833_5', '1880_5',
'1912_5', '1958_5', '2038_5'],
dtype='object')
In [64]:
a = adata_low[adata_low.obs['CAF_500_ZBTB16'] == 'within'].obs.index
b = pd.DataFrame(adata_low[adata_low.obs['CAF_500_ZBTB16'] == 'within'].X.toarray(), index= adata_low[adata_low.obs['CAF_500_ZBTB16'] == 'within'].obs.index, columns = adata_low[adata_low.obs['CAF_500_ZBTB16'] == 'within'].var.index)
b.loc[a,'TIMP1'].mean()
Out[64]:
0.3296684870626034
In [65]:
a = adata_low[adata_low.obs['CAF_500_ZBTB16'] == 'farther'].obs.index
b = pd.DataFrame(adata_low[adata_low.obs['CAF_500_ZBTB16'] == 'farther'].X.toarray(), index= adata_low[adata_low.obs['CAF_500_ZBTB16'] == 'farther'].obs.index, columns = adata_low[adata_low.obs['CAF_500_ZBTB16'] == 'farther'].var.index)
b.loc[a,'TIMP1'].mean()
Out[65]:
0.5171805348741544
In [70]:
adata_high[adata_high.obs.leiden == 'CAF CXCL14']
Out[70]:
View of AnnData object with n_obs × n_vars = 12 × 992
obs: 'fov', 'Area', 'AspectRatio', 'CenterX_global_px', 'CenterY_global_px', 'Width', 'Height', 'Mean.PanCK', 'Max.PanCK', 'Mean.CD68', 'Max.CD68', 'Mean.MembraneStain_B2M', 'Max.MembraneStain_B2M', 'Mean.CD45', 'Max.CD45', 'Mean.DAPI', 'Max.DAPI', 'Patients', 'group', 'cell_ID', 'n_genes_by_counts', 'log1p_n_genes_by_counts', 'total_counts', 'log1p_total_counts', 'pct_counts_in_top_50_genes', 'pct_counts_in_top_100_genes', 'pct_counts_in_top_200_genes', 'pct_counts_in_top_500_genes', 'total_counts_NegPrb', 'log1p_total_counts_NegPrb', 'pct_counts_NegPrb', 'n_counts', 'leiden', 'new_leiden', 'level', 'disease', 'T_CAF', 'clusters_ordered', 'within_100', 'epithelial_100_ZBTB16', 'fibroblast_100_ZBTB16', 'CAF_100_ZBTB16', 'CAF CXCL14_100_ZBTB16', 'within_500', 'epithelial_500_ZBTB16', 'fibroblast_500_ZBTB16', 'CAF_500_ZBTB16', 'CAF CXCL14_500_ZBTB16', 'within_1000', 'epithelial_1000_ZBTB16', 'fibroblast_1000_ZBTB16', 'CAF_1000_ZBTB16', 'CAF CXCL14_1000_ZBTB16'
var: 'NegPrb', 'n_cells_by_counts', 'mean_counts', 'log1p_mean_counts', 'pct_dropout_by_counts', 'total_counts', 'log1p_total_counts'
uns: 'spatial', 'log1p', 'pca', 'neighbors', 'umap', 'leiden', 'leiden_colors', 'new_leiden_colors'
obsm: 'spatial', 'spatial_fov', 'X_pca', 'X_umap'
varm: 'PCs'
obsp: 'distances', 'connectivities'
In [71]:
adata_high[adata_high.obs['CAF CXCL14_500_ZBTB16'] =='farther']
Out[71]:
View of AnnData object with n_obs × n_vars = 10 × 992
obs: 'fov', 'Area', 'AspectRatio', 'CenterX_global_px', 'CenterY_global_px', 'Width', 'Height', 'Mean.PanCK', 'Max.PanCK', 'Mean.CD68', 'Max.CD68', 'Mean.MembraneStain_B2M', 'Max.MembraneStain_B2M', 'Mean.CD45', 'Max.CD45', 'Mean.DAPI', 'Max.DAPI', 'Patients', 'group', 'cell_ID', 'n_genes_by_counts', 'log1p_n_genes_by_counts', 'total_counts', 'log1p_total_counts', 'pct_counts_in_top_50_genes', 'pct_counts_in_top_100_genes', 'pct_counts_in_top_200_genes', 'pct_counts_in_top_500_genes', 'total_counts_NegPrb', 'log1p_total_counts_NegPrb', 'pct_counts_NegPrb', 'n_counts', 'leiden', 'new_leiden', 'level', 'disease', 'T_CAF', 'clusters_ordered', 'within_100', 'epithelial_100_ZBTB16', 'fibroblast_100_ZBTB16', 'CAF_100_ZBTB16', 'CAF CXCL14_100_ZBTB16', 'within_500', 'epithelial_500_ZBTB16', 'fibroblast_500_ZBTB16', 'CAF_500_ZBTB16', 'CAF CXCL14_500_ZBTB16', 'within_1000', 'epithelial_1000_ZBTB16', 'fibroblast_1000_ZBTB16', 'CAF_1000_ZBTB16', 'CAF CXCL14_1000_ZBTB16'
var: 'NegPrb', 'n_cells_by_counts', 'mean_counts', 'log1p_mean_counts', 'pct_dropout_by_counts', 'total_counts', 'log1p_total_counts'
uns: 'spatial', 'log1p', 'pca', 'neighbors', 'umap', 'leiden', 'leiden_colors', 'new_leiden_colors'
obsm: 'spatial', 'spatial_fov', 'X_pca', 'X_umap'
varm: 'PCs'
obsp: 'distances', 'connectivities'
In [79]:
adata_low[adata_low.obs['epithelial_500_ZBTB16'] =='farther']
Out[79]:
View of AnnData object with n_obs × n_vars = 2840 × 992
obs: 'fov', 'Area', 'AspectRatio', 'CenterX_global_px', 'CenterY_global_px', 'Width', 'Height', 'Mean.PanCK', 'Max.PanCK', 'Mean.CD68', 'Max.CD68', 'Mean.MembraneStain_B2M', 'Max.MembraneStain_B2M', 'Mean.CD45', 'Max.CD45', 'Mean.DAPI', 'Max.DAPI', 'Patients', 'group', 'cell_ID', 'n_genes_by_counts', 'log1p_n_genes_by_counts', 'total_counts', 'log1p_total_counts', 'pct_counts_in_top_50_genes', 'pct_counts_in_top_100_genes', 'pct_counts_in_top_200_genes', 'pct_counts_in_top_500_genes', 'total_counts_NegPrb', 'log1p_total_counts_NegPrb', 'pct_counts_NegPrb', 'n_counts', 'leiden', 'new_leiden', 'level', 'disease', 'T_CAF', 'clusters_ordered', 'within_100', 'epithelial_100_ZBTB16', 'fibroblast_100_ZBTB16', 'CAF_100_ZBTB16', 'CAF CXCL14_100_ZBTB16', 'within_500', 'epithelial_500_ZBTB16', 'fibroblast_500_ZBTB16', 'CAF_500_ZBTB16', 'CAF CXCL14_500_ZBTB16', 'within_1000', 'epithelial_1000_ZBTB16', 'fibroblast_1000_ZBTB16', 'CAF_1000_ZBTB16', 'CAF CXCL14_1000_ZBTB16'
var: 'NegPrb', 'n_cells_by_counts', 'mean_counts', 'log1p_mean_counts', 'pct_dropout_by_counts', 'total_counts', 'log1p_total_counts'
uns: 'spatial', 'log1p', 'pca', 'neighbors', 'umap', 'leiden', 'leiden_colors', 'new_leiden_colors'
obsm: 'spatial', 'spatial_fov', 'X_pca', 'X_umap'
varm: 'PCs'
obsp: 'distances', 'connectivities'
Compare distance between cell types in high and low iNKT22 conditions¶
In [80]:
sc.pl.umap(
adata_low, show=False,
color= "leiden", palette=sns.color_palette(palette),
title = 'Low iNKT22'
)
plt.savefig('/Users/paolamaragno/Library/CloudStorage/OneDrive-UniversitàdegliStudidiMilano/Policlinico/analisi/spatial/spatial/K/results_seed/umap_clusters_named_low.pdf',bbox_inches="tight")
/opt/anaconda3/envs/spatial_new/lib/python3.9/site-packages/anndata/_core/anndata.py:1145: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual. df[key] = c /opt/anaconda3/envs/spatial_new/lib/python3.9/site-packages/anndata/_core/anndata.py:1145: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual. df[key] = c /opt/anaconda3/envs/spatial_new/lib/python3.9/site-packages/anndata/_core/anndata.py:1145: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual. df[key] = c /opt/anaconda3/envs/spatial_new/lib/python3.9/site-packages/anndata/_core/anndata.py:1145: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual. df[key] = c /opt/anaconda3/envs/spatial_new/lib/python3.9/site-packages/anndata/_core/anndata.py:1145: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual. df[key] = c /opt/anaconda3/envs/spatial_new/lib/python3.9/site-packages/anndata/_core/anndata.py:1145: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual. df[key] = c /opt/anaconda3/envs/spatial_new/lib/python3.9/site-packages/anndata/_core/anndata.py:1145: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual. df[key] = c /opt/anaconda3/envs/spatial_new/lib/python3.9/site-packages/anndata/_core/anndata.py:1145: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual. df[key] = c /opt/anaconda3/envs/spatial_new/lib/python3.9/site-packages/anndata/_core/anndata.py:1145: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual. df[key] = c /opt/anaconda3/envs/spatial_new/lib/python3.9/site-packages/anndata/_core/anndata.py:1145: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual. df[key] = c /opt/anaconda3/envs/spatial_new/lib/python3.9/site-packages/anndata/_core/anndata.py:1145: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual. df[key] = c /opt/anaconda3/envs/spatial_new/lib/python3.9/site-packages/anndata/_core/anndata.py:1145: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual. df[key] = c /opt/anaconda3/envs/spatial_new/lib/python3.9/site-packages/anndata/_core/anndata.py:1145: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual. df[key] = c /opt/anaconda3/envs/spatial_new/lib/python3.9/site-packages/anndata/_core/anndata.py:1145: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual. df[key] = c /opt/anaconda3/envs/spatial_new/lib/python3.9/site-packages/anndata/_core/anndata.py:1145: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual. df[key] = c
In [50]:
sc.pl.umap(
adata_high, show=False,
color= "leiden", palette=sns.color_palette(palette),
title = 'High iNKT22'
)
plt.savefig('/Users/paolamaragno/Library/CloudStorage/OneDrive-UniversitàdegliStudidiMilano/Policlinico/analisi/spatial/spatial/K/results_seed/umap_clusters_named_high.pdf',bbox_inches="tight")
/opt/anaconda3/envs/spatial_new/lib/python3.9/site-packages/scanpy/plotting/_utils.py:471: ImplicitModificationWarning: Trying to modify attribute `._uns` of view, initializing view as actual. adata.uns[value_to_plot + "_colors"] = colors_list
In [46]:
sq.gr.spatial_neighbors(adata_low, coord_type="generic", spatial_key="spatial")
sq.gr.nhood_enrichment(adata_low, cluster_key="leiden")
sq.gr.spatial_neighbors(adata_high, coord_type="generic", spatial_key="spatial")
sq.gr.nhood_enrichment(adata_high, cluster_key="leiden")
/opt/anaconda3/envs/spatial_new/lib/python3.9/site-packages/squidpy/gr/_utils.py:194: ImplicitModificationWarning: Setting element `.obsp['spatial_connectivities']` of view, initializing view as actual. obj[key] = data
0%| | 0/1000 [00:00<?, ?/s]
/opt/anaconda3/envs/spatial_new/lib/python3.9/site-packages/numba/core/decorators.py:246: RuntimeWarning: nopython is set for njit and is ignored
warnings.warn('nopython is set for njit and is ignored', RuntimeWarning)
/opt/anaconda3/envs/spatial_new/lib/python3.9/site-packages/squidpy/gr/_utils.py:194: ImplicitModificationWarning: Setting element `.obsp['spatial_connectivities']` of view, initializing view as actual.
obj[key] = data
0%| | 0/1000 [00:00<?, ?/s]
/opt/anaconda3/envs/spatial_new/lib/python3.9/site-packages/numba/core/decorators.py:246: RuntimeWarning: nopython is set for njit and is ignored
warnings.warn('nopython is set for njit and is ignored', RuntimeWarning)
In [82]:
# plotted values are in adata_low.uns['leiden_nhood_enrichment']['zscore']
sq.pl.nhood_enrichment(
adata_low,
cluster_key="leiden",
method="average",
cmap='OrRd',
figsize=(5, 5),
title = 'Low iNKT22'
)
plt.savefig('/Users/paolamaragno/Library/CloudStorage/OneDrive-UniversitàdegliStudidiMilano/Policlinico/analisi/spatial/spatial/K/results_seed/neighborhood_low.pdf',bbox_inches="tight")
In [83]:
# plotted values are in adata_low.uns['leiden_nhood_enrichment']['zscore']
sq.pl.nhood_enrichment(
adata_high,
cluster_key="leiden",
method="average",
cmap='OrRd',
figsize=(5, 5),
title = 'High iNKT22'
)
plt.savefig('/Users/paolamaragno/Library/CloudStorage/OneDrive-UniversitàdegliStudidiMilano/Policlinico/analisi/spatial/spatial/K/results_seed/neighborhood_high.pdf',bbox_inches="tight")
In [32]:
# for each fov, for each possible comparison between cell-types report the minkovski distance
"""
distance_per_cell_type_low_all = {}
for fov in pd.unique(adata_low.obs.fov):
adata_fov = adata_low[adata_low.obs.fov == fov]
pos_fov = adata_fov.obs[['CenterX_global_px','CenterY_global_px']]
dm_fov = distance_matrix(pos_fov, pos_fov)
dm_fov = tril(dm_fov).toarray()
distances_fov = pd.DataFrame(dm_fov, index = adata_fov.obs.leiden, columns = adata_fov.obs.leiden)
for r in range(1,adata_fov.shape[0]):
if r == 1:
c= 0
combination1 = distances_fov.columns.values[c] + '-' + distances_fov.index.values[r]
combination2 = distances_fov.index.values[r] + '-' + distances_fov.columns.values[c]
if combination1 in distance_per_cell_type_low_all.keys() :
distance_per_cell_type_low_all[combination1].append(distances_fov.iat[r,c])
elif combination2 in distance_per_cell_type_low_all.keys():
distance_per_cell_type_low_all[combination2].append(distances_fov.iat[r,c])
else:
distance_per_cell_type_low_all[combination1] = [distances_fov.iat[r,c]]
else:
for c in range(r):
combination1 = distances_fov.columns.values[c] + '-' + distances_fov.index.values[r]
combination2 = distances_fov.index.values[r] + '-' + distances_fov.columns.values[c]
if combination1 in distance_per_cell_type_low_all.keys() :
distance_per_cell_type_low_all[combination1].append(distances_fov.iat[r,c])
elif combination2 in distance_per_cell_type_low_all.keys():
distance_per_cell_type_low_all[combination2].append(distances_fov.iat[r,c])
else:
distance_per_cell_type_low_all[combination1] = [distances_fov.iat[r,c]]
"""
In [33]:
# for each fov, for each possible comparison between cell-types report the minkovski distance
"""
distance_per_cell_type_high_all = {}
for fov in pd.unique(adata_high.obs.fov):
adata_fov = adata_high[adata_high.obs.fov == fov]
pos_fov = adata_fov.obs[['CenterX_global_px','CenterY_global_px']]
dm_fov = distance_matrix(pos_fov, pos_fov)
dm_fov = tril(dm_fov).toarray()
distances_fov = pd.DataFrame(dm_fov, index = adata_fov.obs.leiden, columns = adata_fov.obs.leiden)
for r in range(1,adata_fov.shape[0]):
if r == 1:
c= 0
combination1 = distances_fov.columns.values[c] + '-' + distances_fov.index[r]
combination2 = distances_fov.index[r] + '-' + distances_fov.columns.values[c]
if combination1 in distance_per_cell_type_high_all.keys() :
distance_per_cell_type_high_all[combination1].append(distances_fov.iat[r,c])
elif combination2 in distance_per_cell_type_high_all.keys():
distance_per_cell_type_high_all[combination2].append(distances_fov.iat[r,c])
else:
distance_per_cell_type_high_all[combination1] = [distances_fov.iat[r,c]]
else:
for c in range(r):
combination1 = distances_fov.columns.values[c] + '-' + distances_fov.index[r]
combination2 = distances_fov.index[r] + '-' + distances_fov.columns.values[c]
if combination1 in distance_per_cell_type_high_all.keys() :
distance_per_cell_type_high_all[combination1].append(distances_fov.iat[r,c])
elif combination2 in distance_per_cell_type_high_all.keys():
distance_per_cell_type_high_all[combination2].append(distances_fov.iat[r,c])
else:
distance_per_cell_type_high_all[combination1] = [distances_fov.iat[r,c]]
"""
In [34]:
# compute the t-test between the distribution of distances between two cell types in high and in low.
# bonferroni correction
"""
from scipy import stats
from statsmodels.sandbox.stats.multicomp import multipletests
t_test_results = pd.DataFrame(columns=['comparison', 'statistic', 'pvalue'])
# loop over column_list and execute code explained above
for combination in distance_per_cell_type_high_all.keys():
combination2 = combination.replace('-', ' ').split(' ')[1] + '-' + combination.replace('-', ' ').split(' ')[0]
if combination in distance_per_cell_type_low_all.keys():
low = distance_per_cell_type_low_all[combination]
high = distance_per_cell_type_high_all[combination]
new_row = [combination, stats.ttest_ind(low,high).statistic, stats.ttest_ind(low,high).pvalue]
t_test_results.loc[len(t_test_results)] = new_row
elif combination2 in distance_per_cell_type_low_all.keys():
low = distance_per_cell_type_low_all[combination2]
high = distance_per_cell_type_high_all[combination]
new_row = [combination, stats.ttest_ind(low,high).statistic, stats.ttest_ind(low,high).pvalue]
t_test_results.loc[len(t_test_results)] = new_row
rejected, p_adjusted, _, alpha_corrected = multipletests(t_test_results['pvalue'].values, alpha=0.05,
method='bonferroni', is_sorted=False, returnsorted=False)
t_test_results['p_adjusted'] = p_adjusted
t_test_results.to_csv('/Users/paolamaragno/Library/CloudStorage/OneDrive-UniversitàdegliStudidiMilano/Policlinico/analisi/spatial/spatial/K/results_K/comparisons_distance_high_low.txt', index =False)
t_test_results
"""
Out[34]:
| comparison | statistic | pvalue | p_adjusted | |
|---|---|---|---|---|
| 0 | Pericyte-Pericyte | 3.899336 | 9.658519e-05 | 6.084867e-03 |
| 1 | Pericyte-CAF | -13.942503 | 4.053412e-44 | 2.553650e-42 |
| 2 | CAF-Plasma IgG | -4.560603 | 5.104214e-06 | 3.215655e-04 |
| 3 | Pericyte-Macrophage | -13.190802 | 1.125090e-39 | 7.088065e-38 |
| 4 | CAF-Macrophage | -2.938060 | 3.302971e-03 | 2.080872e-01 |
| ... | ... | ... | ... | ... |
| 58 | Epithelial-Pericyte | 11.274882 | 3.346029e-29 | 2.107998e-27 |
| 59 | CAF CXCL14-Pericyte | 7.815933 | 6.762087e-15 | 4.260115e-13 |
| 60 | Epithelial-Fibroblast | -5.387545 | 8.869159e-08 | 5.587570e-06 |
| 61 | CAF CXCL14-Fibroblast | 4.253593 | 2.333823e-05 | 1.470308e-03 |
| 62 | CAF CXCL14-CAF CXCL14 | 0.218709 | 8.268772e-01 | 1.000000e+00 |
63 rows × 4 columns
In [35]:
"""np.array(t_test_results['comparison'])"""
Out[35]:
array(['Pericyte-Pericyte', 'Pericyte-CAF', 'CAF-Plasma IgG',
'Pericyte-Macrophage', 'CAF-Macrophage', 'Macrophage-Macrophage',
'Plasma IgG-Plasma IgG', 'Pericyte-B', 'CAF-B', 'Macrophage-B',
'B-B', 'Pericyte-T', 'CAF-T', 'Macrophage-T', 'B-T', 'CAF-CAF',
'T-T', 'CAF-Plasma IgA', 'Plasma IgG-Plasma IgA',
'Macrophage-Plasma IgA', 'T-Plasma IgA', 'Pericyte-Smooth muscle',
'CAF-Smooth muscle', 'Plasma IgG-Smooth muscle',
'Macrophage-Smooth muscle', 'T-Smooth muscle',
'Plasma IgA-Smooth muscle', 'Pericyte-Endothelial',
'CAF-Endothelial', 'Macrophage-Endothelial', 'B-Endothelial',
'T-Endothelial', 'Endothelial-Endothelial',
'Plasma IgA-Plasma IgA', 'Pericyte-Fibroblast', 'CAF-Fibroblast',
'Plasma IgG-Fibroblast', 'Macrophage-Fibroblast', 'B-Fibroblast',
'T-Fibroblast', 'Plasma IgA-Fibroblast',
'Smooth muscle-Fibroblast', 'Endothelial-Fibroblast',
'Macrophage-Epithelial', 'CAF-Epithelial', 'T-Epithelial',
'Plasma IgA-Epithelial', 'B-Epithelial', 'Endothelial-Epithelial',
'Epithelial-Epithelial', 'Epithelial-Smooth muscle',
'Macrophage-CAF CXCL14', 'CAF-CAF CXCL14', 'T-CAF CXCL14',
'Plasma IgA-CAF CXCL14', 'B-CAF CXCL14', 'Endothelial-CAF CXCL14',
'Epithelial-CAF CXCL14', 'Epithelial-Pericyte',
'CAF CXCL14-Pericyte', 'Epithelial-Fibroblast',
'CAF CXCL14-Fibroblast', 'CAF CXCL14-CAF CXCL14'], dtype=object)
In [50]:
"""'Plasma IgG-T' in distance_per_cell_type_high_all.keys()"""
Out[50]:
True
In [52]:
""""
pericyte = pd.DataFrame(data = {'iNKT22 level' : np.concatenate([np.repeat('Low',len(distance_per_cell_type_low_all['T-Pericyte'])),
np.repeat('High',len(distance_per_cell_type_high_all['Pericyte-T']))]),
'Distance' : np.concatenate([distance_per_cell_type_low_all['T-Pericyte'], distance_per_cell_type_high_all['Pericyte-T']])})
pericyte.to_excel('/Users/paolamaragno/Library/CloudStorage/OneDrive-UniversitàdegliStudidiMilano/Policlinico/analisi/spatial/spatial/K/results_K/distances_T/Pericyte.xlsx',
index=None)
CAF = pd.DataFrame(data = {'iNKT22 level' : np.concatenate([np.repeat('Low',len(distance_per_cell_type_low_all['CAF-T'])),
np.repeat('High',len(distance_per_cell_type_high_all['CAF-T']))]),
'Distance' : np.concatenate([distance_per_cell_type_low_all['CAF-T'], distance_per_cell_type_high_all['CAF-T']])})
CAF.to_excel('/Users/paolamaragno/Library/CloudStorage/OneDrive-UniversitàdegliStudidiMilano/Policlinico/analisi/spatial/spatial/K/results_K/distances_T/CAF.xlsx',
index=None)
macrophage = pd.DataFrame(data = {'iNKT22 level' : np.concatenate([np.repeat('Low',len(distance_per_cell_type_low_all['Macrophage-T'])),
np.repeat('High',len(distance_per_cell_type_high_all['Macrophage-T']))]),
'Distance' : np.concatenate([distance_per_cell_type_low_all['Macrophage-T'], distance_per_cell_type_high_all['Macrophage-T']])})
macrophage.to_excel('/Users/paolamaragno/Library/CloudStorage/OneDrive-UniversitàdegliStudidiMilano/Policlinico/analisi/spatial/spatial/K/results_K/distances_T/Macrophage.xlsx',
index=None)
B = pd.DataFrame(data = {'iNKT22 level' : np.concatenate([np.repeat('Low',len(distance_per_cell_type_low_all['T-B'])),
np.repeat('High',len(distance_per_cell_type_high_all['B-T']))]),
'Distance' : np.concatenate([distance_per_cell_type_low_all['T-B'], distance_per_cell_type_high_all['B-T']])})
B.to_excel('/Users/paolamaragno/Library/CloudStorage/OneDrive-UniversitàdegliStudidiMilano/Policlinico/analisi/spatial/spatial/K/results_K/distances_T/B.xlsx',
index=None)
T = pd.DataFrame(data = {'iNKT22 level' : np.concatenate([np.repeat('Low',len(distance_per_cell_type_low_all['T-T'])),
np.repeat('High',len(distance_per_cell_type_high_all['T-T']))]),
'Distance' : np.concatenate([distance_per_cell_type_low_all['T-T'], distance_per_cell_type_high_all['T-T']])})
T.to_excel('/Users/paolamaragno/Library/CloudStorage/OneDrive-UniversitàdegliStudidiMilano/Policlinico/analisi/spatial/spatial/K/results_K/distances_T/T.xlsx',
index=None)
plasmaIgA = pd.DataFrame(data = {'iNKT22 level' : np.concatenate([np.repeat('Low',len(distance_per_cell_type_low_all['T-Plasma IgA'])),
np.repeat('High',len(distance_per_cell_type_high_all['T-Plasma IgA']))]),
'Distance' : np.concatenate([distance_per_cell_type_low_all['T-Plasma IgA'], distance_per_cell_type_high_all['T-Plasma IgA']])})
plasmaIgA.to_excel('/Users/paolamaragno/Library/CloudStorage/OneDrive-UniversitàdegliStudidiMilano/Policlinico/analisi/spatial/spatial/K/results_K/distances_T/Plasma IgA.xlsx',
index=None)
plasmaIgG = pd.DataFrame(data = {'iNKT22 level' : np.concatenate([np.repeat('Low',len(distance_per_cell_type_low_all['T-Plasma IgG'])),
np.repeat('High',len(distance_per_cell_type_high_all['Plasma IgG-T']))]),
'Distance' : np.concatenate([distance_per_cell_type_low_all['T-Plasma IgG'], distance_per_cell_type_high_all['Plasma IgG-T']])})
plasmaIgG.to_excel('/Users/paolamaragno/Library/CloudStorage/OneDrive-UniversitàdegliStudidiMilano/Policlinico/analisi/spatial/spatial/K/results_K/distances_T/Plasma IgG.xlsx',
index=None)
smooth = pd.DataFrame(data = {'iNKT22 level' : np.concatenate([np.repeat('Low',len(distance_per_cell_type_low_all['T-Smooth muscle'])),
np.repeat('High',len(distance_per_cell_type_high_all['T-Smooth muscle']))]),
'Distance' : np.concatenate([distance_per_cell_type_low_all['T-Smooth muscle'], distance_per_cell_type_high_all['T-Smooth muscle']])})
smooth.to_excel('/Users/paolamaragno/Library/CloudStorage/OneDrive-UniversitàdegliStudidiMilano/Policlinico/analisi/spatial/spatial/K/results_K/distances_T/Smooth muscle cell.xlsx',
index=None)
endothelial = pd.DataFrame(data = {'iNKT22 level' : np.concatenate([np.repeat('Low',len(distance_per_cell_type_low_all['Endothelial-T'])),
np.repeat('High',len(distance_per_cell_type_high_all['T-Endothelial']))]),
'Distance' : np.concatenate([distance_per_cell_type_low_all['Endothelial-T'], distance_per_cell_type_high_all['T-Endothelial']])})
endothelial.to_excel('/Users/paolamaragno/Library/CloudStorage/OneDrive-UniversitàdegliStudidiMilano/Policlinico/analisi/spatial/spatial/K/results_K/distances_T/Endothelial.xlsx',
index=None)
fibro = pd.DataFrame(data = {'iNKT22 level' : np.concatenate([np.repeat('Low',len(distance_per_cell_type_low_all['T-Fibroblast'])),
np.repeat('High',len(distance_per_cell_type_high_all['T-Fibroblast']))]),
'Distance' : np.concatenate([distance_per_cell_type_low_all['T-Fibroblast'], distance_per_cell_type_high_all['T-Fibroblast']])})
fibro.to_excel('/Users/paolamaragno/Library/CloudStorage/OneDrive-UniversitàdegliStudidiMilano/Policlinico/analisi/spatial/spatial/K/results_K/distances_T/Fibroblast.xlsx',
index=None)
epi = pd.DataFrame(data = {'iNKT22 level' : np.concatenate([np.repeat('Low',len(distance_per_cell_type_low_all['T-Epithelial'])),
np.repeat('High',len(distance_per_cell_type_high_all['T-Epithelial']))]),
'Distance' : np.concatenate([distance_per_cell_type_low_all['T-Epithelial'], distance_per_cell_type_high_all['T-Epithelial']])})
epi.to_excel('/Users/paolamaragno/Library/CloudStorage/OneDrive-UniversitàdegliStudidiMilano/Policlinico/analisi/spatial/spatial/K/results_K/distances_T/Epithelial.xlsx',
index=None)
CAF_CXCL14 = pd.DataFrame(data = {'iNKT22 level' : np.concatenate([np.repeat('Low',len(distance_per_cell_type_low_all['CAF CXCL14-T'])),
np.repeat('High',len(distance_per_cell_type_high_all['T-CAF CXCL14']))]),
'Distance' : np.concatenate([distance_per_cell_type_low_all['CAF CXCL14-T'], distance_per_cell_type_high_all['T-CAF CXCL14']])})
CAF_CXCL14.to_excel('/Users/paolamaragno/Library/CloudStorage/OneDrive-UniversitàdegliStudidiMilano/Policlinico/analisi/spatial/spatial/K/results_K/distances_T/CAF CXCL14.xlsx',
index=None)
"""
In [53]:
# compute, for each pair of cell-types, the mean distance considering all the distances from all the fovs
"""
distance_per_cell_type_low_mean = {}
for k in distance_per_cell_type_low_all.keys():
distance_per_cell_type_low_mean[k] = np.mean(distance_per_cell_type_low_all[k])
distance_per_cell_type_low_mean_df = pd.DataFrame(columns = ['cell_type1', 'cell_type2', 'mean'])
for i in distance_per_cell_type_low_mean.items():
new_row = [i[0].split('-')[0], i[0].split('-')[1], i[1]]
distance_per_cell_type_low_mean_df.loc[len(distance_per_cell_type_low_mean_df)] = new_row
distance_per_cell_type_low_mean_df
distance_per_cell_type_low_mean_df.to_csv('/Users/paolamaragno/Library/CloudStorage/OneDrive-UniversitàdegliStudidiMilano/Policlinico/analisi/spatial/spatial/K/results_K/distances_low.txt', index =False)
"""
In [54]:
# compute, for each pair of cell-types, the mean distance considering all the distances from all the fovs
"""
distance_per_cell_type_high_mean = {}
for k in distance_per_cell_type_high_all.keys():
distance_per_cell_type_high_mean[k] = np.mean(distance_per_cell_type_high_all[k])
distance_per_cell_type_high_mean_df = pd.DataFrame(columns = ['cell_type1', 'cell_type2', 'mean'])
for i in distance_per_cell_type_high_mean.items():
new_row = [i[0].split('-')[0], i[0].split('-')[1], i[1]]
distance_per_cell_type_high_mean_df.loc[len(distance_per_cell_type_high_mean_df)] = new_row
distance_per_cell_type_high_mean_df
distance_per_cell_type_high_mean_df.to_csv('/Users/paolamaragno/Library/CloudStorage/OneDrive-UniversitàdegliStudidiMilano/Policlinico/analisi/spatial/spatial/K/results_K/distances_high.txt', index =False)
"""
In [47]:
# for each fov, compute the mean distance between each pair of cell-types in that fov
distance_per_cell_type_low_fov_mean = {}
for fov in pd.unique(adata_low.obs.fov):
adata_fov = adata_low[adata_low.obs.fov == fov]
pos_fov = adata_fov.obs[['CenterX_global_px','CenterY_global_px']]
dm_fov = distance_matrix(pos_fov, pos_fov)
dm_fov = tril(dm_fov).toarray()
distances_fov = pd.DataFrame(dm_fov, index = adata_fov.obs.leiden, columns = adata_fov.obs.leiden)
distance_per_cell_type_low = {}
for r in range(1,adata_fov.shape[0]):
if r == 1:
c= 0
combination1 = distances_fov.columns.values[c] + '-' + distances_fov.index[r]
combination2 = distances_fov.index[r] + '-' + distances_fov.columns.values[c]
if combination1 in distance_per_cell_type_low.keys() :
distance_per_cell_type_low[combination1].append(distances_fov.iat[r,c])
elif combination2 in distance_per_cell_type_low.keys():
distance_per_cell_type_low[combination2].append(distances_fov.iat[r,c])
else:
distance_per_cell_type_low[combination1] = [distances_fov.iat[r,c]]
else:
for c in range(r):
combination1 = distances_fov.columns.values[c] + '-' + distances_fov.index[r]
combination2 = distances_fov.index[r] + '-' + distances_fov.columns.values[c]
if combination1 in distance_per_cell_type_low.keys() :
distance_per_cell_type_low[combination1].append(distances_fov.iat[r,c])
elif combination2 in distance_per_cell_type_low.keys():
distance_per_cell_type_low[combination2].append(distances_fov.iat[r,c])
else:
distance_per_cell_type_low[combination1] = [distances_fov.iat[r,c]]
for combination in distance_per_cell_type_low.keys():
combination2 = combination.split('-')[1] +'-'+combination.split('-')[0]
if combination in distance_per_cell_type_low_fov_mean.keys():
distance_per_cell_type_low_fov_mean[combination].append(np.mean(distance_per_cell_type_low[combination]))
elif combination2 in distance_per_cell_type_low_fov_mean.keys():
distance_per_cell_type_low_fov_mean[combination2].append(np.mean(distance_per_cell_type_low[combination]))
else:
distance_per_cell_type_low_fov_mean[combination] = [np.mean(distance_per_cell_type_low[combination])]
In [48]:
# compute, for each pair of cell-types, the mean distance betweeb the mean distances of the different fovs
distance_per_cell_type_high_fov_mean = {}
for fov in pd.unique(adata_high.obs.fov):
adata_fov = adata_high[adata_high.obs.fov == fov]
pos_fov = adata_fov.obs[['CenterX_global_px','CenterY_global_px']]
dm_fov = distance_matrix(pos_fov, pos_fov)
dm_fov = tril(dm_fov).toarray()
distances_fov = pd.DataFrame(dm_fov, index = adata_fov.obs.leiden, columns = adata_fov.obs.leiden)
distance_per_cell_type_high = {}
for r in range(1,adata_fov.shape[0]):
if r == 1:
c= 0
combination1 = distances_fov.columns.values[c] + '-' + distances_fov.index[r]
combination2 = distances_fov.index[r] + '-' + distances_fov.columns.values[c]
if combination1 in distance_per_cell_type_high.keys() :
distance_per_cell_type_high[combination1].append(distances_fov.iat[r,c])
elif combination2 in distance_per_cell_type_high.keys():
distance_per_cell_type_high[combination2].append(distances_fov.iat[r,c])
else:
distance_per_cell_type_high[combination1] = [distances_fov.iat[r,c]]
else:
for c in range(r):
combination1 = distances_fov.columns.values[c] + '-' + distances_fov.index[r]
combination2 = distances_fov.index[r] + '-' + distances_fov.columns.values[c]
if combination1 in distance_per_cell_type_high.keys() :
distance_per_cell_type_high[combination1].append(distances_fov.iat[r,c])
elif combination2 in distance_per_cell_type_high.keys():
distance_per_cell_type_high[combination2].append(distances_fov.iat[r,c])
else:
distance_per_cell_type_high[combination1] = [distances_fov.iat[r,c]]
for combination in distance_per_cell_type_high.keys():
combination2 = combination.split('-')[1] +'-'+combination.split('-')[0]
if combination in distance_per_cell_type_high_fov_mean.keys():
distance_per_cell_type_high_fov_mean[combination].append(np.mean(distance_per_cell_type_high[combination]))
elif combination2 in distance_per_cell_type_high_fov_mean.keys():
distance_per_cell_type_high_fov_mean[combination2].append(np.mean(distance_per_cell_type_high[combination]))
else:
distance_per_cell_type_high_fov_mean[combination] = [np.mean(distance_per_cell_type_high[combination])]
In [49]:
# compute, for each pair of cell-types, the mean distance between the mean distances of the different fovs
distance_per_cell_type_low_fov_mean2 = {}
for k in distance_per_cell_type_low_fov_mean.keys():
distance_per_cell_type_low_fov_mean2[k] = np.mean(distance_per_cell_type_low_fov_mean[k])
distance_per_cell_type_low_fov_mean2_df = pd.DataFrame(columns = ['cell_type1', 'cell_type2', 'mean'])
for i in distance_per_cell_type_low_fov_mean2.items():
new_row = [i[0].split('-')[0], i[0].split('-')[1], i[1]]
distance_per_cell_type_low_fov_mean2_df.loc[len(distance_per_cell_type_low_fov_mean2_df)] = new_row
distance_per_cell_type_low_fov_mean2_df.to_csv('/Users/paolamaragno/Library/CloudStorage/OneDrive-UniversitàdegliStudidiMilano/Policlinico/analisi/spatial/spatial/K/results_seed/distances_low_mean.txt', index =False)
distance_per_cell_type_low_fov_mean2_df
Out[49]:
| cell_type1 | cell_type2 | mean | |
|---|---|---|---|
| 0 | Not annotated | Macrophage | 2321.260230 |
| 1 | Not annotated | T | 2296.574468 |
| 2 | Macrophage | T | 2246.230769 |
| 3 | Not annotated | Not annotated | 2160.184728 |
| 4 | Macrophage | Macrophage | 2177.852122 |
| ... | ... | ... | ... |
| 147 | Mast cell | Pericyte | 2957.805310 |
| 148 | Plasma IgA | CAF CXCL14 | 2320.534728 |
| 149 | Mast cell | CAF CXCL14 | 1965.498892 |
| 150 | Epithelial | Plasma IgA | 2581.418119 |
| 151 | Mast cell | Epithelial | 2113.691159 |
152 rows × 3 columns
In [50]:
# compute, for each pair of cell-types, the mean distance between the mean distances of the different fovs
distance_per_cell_type_high_fov_mean2 = {}
for k in distance_per_cell_type_high_fov_mean.keys():
distance_per_cell_type_high_fov_mean2[k] = np.mean(distance_per_cell_type_high_fov_mean[k])
distance_per_cell_type_high_fov_mean2_df = pd.DataFrame(columns = ['cell_type1', 'cell_type2', 'mean'])
for i in distance_per_cell_type_high_fov_mean2.items():
new_row = [i[0].split('-')[0], i[0].split('-')[1], i[1]]
distance_per_cell_type_high_fov_mean2_df.loc[len(distance_per_cell_type_high_fov_mean2_df)] = new_row
distance_per_cell_type_high_fov_mean2_df.to_csv('/Users/paolamaragno/Library/CloudStorage/OneDrive-UniversitàdegliStudidiMilano/Policlinico/analisi/spatial/spatial/K/results_seed/distances_high_mean.txt', index =False)
distance_per_cell_type_high_fov_mean2_df
Out[50]:
| cell_type1 | cell_type2 | mean | |
|---|---|---|---|
| 0 | Pericyte | Pericyte | 2059.801080 |
| 1 | Pericyte | CAF | 2240.851758 |
| 2 | Pericyte | Macrophage | 2325.923209 |
| 3 | CAF | Macrophage | 2195.468712 |
| 4 | Pericyte | T | 2262.483129 |
| ... | ... | ... | ... |
| 148 | Plasma IgA | Goblet | 1904.761090 |
| 149 | CAF CXCL14 | Goblet | 1491.498452 |
| 150 | Paneth | Goblet | 1424.893268 |
| 151 | Smooth muscle | Goblet | 2148.851027 |
| 152 | Goblet | Goblet | 1351.836740 |
153 rows × 3 columns
In [51]:
# for each pair of cell-types, compute the wilcoxon test between the distribution of mean distances in the fovs of high and low conditions
# fdr correction
from scipy import stats
from statsmodels.sandbox.stats.multicomp import multipletests
t_test_results_mean = pd.DataFrame(columns=['comparison', 'statistic', 'pvalue'])
# loop over column_list and execute code explained above
for combination in distance_per_cell_type_high_fov_mean.keys():
combination2 = combination.replace('-', ' ').split(' ')[1] + '-' + combination.replace('-', ' ').split(' ')[0]
if combination in distance_per_cell_type_low_fov_mean.keys():
low = distance_per_cell_type_low_fov_mean[combination]
high = distance_per_cell_type_high_fov_mean[combination]
new_row = [combination, stats.ttest_ind(low,high).statistic, stats.ttest_ind(low,high).pvalue]
t_test_results_mean.loc[len(t_test_results_mean)] = new_row
elif combination2 in distance_per_cell_type_low_fov_mean.keys():
low = distance_per_cell_type_low_fov_mean[combination2]
high = distance_per_cell_type_high_fov_mean[combination]
new_row = [combination, stats.ttest_ind(low,high).statistic, stats.ttest_ind(low,high).pvalue]
t_test_results_mean.loc[len(t_test_results_mean)] = new_row
t_test_results_mean= t_test_results_mean.dropna()
rejected, p_adjusted, _, alpha_corrected = multipletests(t_test_results_mean['pvalue'].values, alpha=0.05,
method='fdr_bh', is_sorted=False, returnsorted=False)
t_test_results_mean['p_adjusted'] = p_adjusted
t_test_results_mean.to_csv('/Users/paolamaragno/Library/CloudStorage/OneDrive-UniversitàdegliStudidiMilano/Policlinico/analisi/spatial/spatial/K/results_seed/comparisons_distance_high_low_mean.txt', index =False)
t_test_results_mean
Out[51]:
| comparison | statistic | pvalue | p_adjusted | |
|---|---|---|---|---|
| 0 | Pericyte-Pericyte | 0.387213 | 0.704421 | 0.922163 |
| 1 | Pericyte-CAF | -0.500304 | 0.624633 | 0.922163 |
| 2 | Pericyte-Macrophage | 0.353361 | 0.729082 | 0.922163 |
| 3 | CAF-Macrophage | 1.131861 | 0.275463 | 0.922163 |
| 4 | Pericyte-T | 0.282518 | 0.781680 | 0.926922 |
| ... | ... | ... | ... | ... |
| 117 | Fibroblast-Goblet | 0.950826 | 0.373359 | 0.922163 |
| 118 | Epithelial-Goblet | 1.005657 | 0.360739 | 0.922163 |
| 119 | CAF CXCL14-Goblet | 2.229272 | 0.061036 | 0.555385 |
| 120 | Paneth-Goblet | 0.215760 | 0.837701 | 0.946291 |
| 121 | Goblet-Goblet | 0.443672 | 0.675821 | 0.922163 |
122 rows × 4 columns
In [52]:
np.array(t_test_results_mean['comparison'])
Out[52]:
array(['Pericyte-Pericyte', 'Pericyte-CAF', 'Pericyte-Macrophage',
'CAF-Macrophage', 'Pericyte-T', 'CAF-T', 'Macrophage-T',
'Macrophage-Macrophage', 'T-T', 'Pericyte-Fibroblast',
'CAF-Fibroblast', 'Macrophage-Fibroblast', 'T-Fibroblast',
'Pericyte-B', 'CAF-B', 'Macrophage-B', 'T-B', 'Fibroblast-B',
'CAF-CAF', 'Fibroblast-Fibroblast', 'Pericyte-Plasma IgG',
'CAF-Plasma IgG', 'Macrophage-Plasma IgG', 'T-Plasma IgG',
'Fibroblast-Plasma IgG', 'Not annotated-Plasma IgG', 'B-B',
'Not annotated-Not annotated', 'Plasma IgG-Plasma IgG',
'Pericyte-Smooth muscle', 'CAF-Smooth muscle',
'Macrophage-Smooth muscle', 'T-Smooth muscle',
'Fibroblast-Smooth muscle', 'B-Smooth muscle',
'Not annotated-Smooth muscle', 'Plasma IgG-Smooth muscle',
'CAF-Mast cell', 'Macrophage-Mast cell', 'T-Mast cell',
'Fibroblast-Mast cell', 'B-Mast cell', 'Not annotated-Mast cell',
'Plasma IgG-Mast cell', 'Smooth muscle-Mast cell',
'Pericyte-CAF CXCL14', 'CAF-CAF CXCL14', 'Macrophage-CAF CXCL14',
'T-CAF CXCL14', 'Fibroblast-CAF CXCL14', 'B-CAF CXCL14',
'Not annotated-CAF CXCL14', 'Mast cell-CAF CXCL14',
'Smooth muscle-Smooth muscle', 'Pericyte-Endothelial',
'CAF-Endothelial', 'Macrophage-Endothelial', 'T-Endothelial',
'Fibroblast-Endothelial', 'B-Endothelial',
'Not annotated-Endothelial', 'CAF CXCL14-Endothelial',
'Endothelial-Endothelial', 'Pericyte-Monocyte', 'CAF-Monocyte',
'Macrophage-Monocyte', 'T-Monocyte', 'Fibroblast-Monocyte',
'B-Monocyte', 'Not annotated-Monocyte', 'CAF CXCL14-Monocyte',
'Endothelial-Monocyte', 'Monocyte-Monocyte', 'Pericyte-Paneth',
'CAF-Paneth', 'Macrophage-Paneth', 'T-Paneth', 'Fibroblast-Paneth',
'B-Paneth', 'Not annotated-Paneth', 'Endothelial-Paneth',
'Monocyte-Paneth', 'CAF CXCL14-CAF CXCL14', 'B-Epithelial',
'Macrophage-Epithelial', 'T-Epithelial',
'Not annotated-Epithelial', 'Pericyte-Epithelial',
'Mast cell-Epithelial', 'CAF-Epithelial', 'Endothelial-Epithelial',
'Monocyte-Epithelial', 'Fibroblast-Epithelial',
'Epithelial-Epithelial', 'B-Plasma IgA', 'Macrophage-Plasma IgA',
'T-Plasma IgA', 'Not annotated-Plasma IgA', 'CAF-Plasma IgA',
'Endothelial-Plasma IgA', 'Monocyte-Plasma IgA',
'Epithelial-Plasma IgA', 'Epithelial-CAF CXCL14',
'Plasma IgA-CAF CXCL14', 'Epithelial-Paneth',
'Plasma IgA-Plasma IgA', 'Epithelial-Smooth muscle',
'Plasma IgA-Smooth muscle', 'Paneth-Paneth', 'B-Goblet',
'Macrophage-Goblet', 'T-Goblet', 'Not annotated-Goblet',
'Pericyte-Goblet', 'CAF-Goblet', 'Endothelial-Goblet',
'Monocyte-Goblet', 'Fibroblast-Goblet', 'Epithelial-Goblet',
'CAF CXCL14-Goblet', 'Paneth-Goblet', 'Goblet-Goblet'],
dtype=object)
In [102]:
'CAF-T' in distance_per_cell_type_low_fov_mean.keys()
Out[102]:
False
In [99]:
'CAF-T' in distance_per_cell_type_high_fov_mean.keys()
Out[99]:
True
In [103]:
pericyte = pd.DataFrame(data = {'iNKT22 level' : np.concatenate([np.repeat('Low',len(distance_per_cell_type_low_fov_mean['T-Pericyte'])),
np.repeat('High',len(distance_per_cell_type_high_fov_mean['Pericyte-T']))]),
'Distance' : np.concatenate([distance_per_cell_type_low_fov_mean['T-Pericyte'], distance_per_cell_type_high_fov_mean['Pericyte-T']])})
pericyte.to_excel('/Users/paolamaragno/Library/CloudStorage/OneDrive-UniversitàdegliStudidiMilano/Policlinico/analisi/spatial/spatial/K/results_seed/distances_T_mean/Pericyte.xlsx',
index=None)
paneth = pd.DataFrame(data = {'iNKT22 level' : np.concatenate([np.repeat('Low',len(distance_per_cell_type_low_fov_mean['T-Paneth'])),
np.repeat('High',len(distance_per_cell_type_high_fov_mean['T-Paneth']))]),
'Distance' : np.concatenate([distance_per_cell_type_low_fov_mean['T-Paneth'], distance_per_cell_type_high_fov_mean['T-Paneth']])})
paneth.to_excel('/Users/paolamaragno/Library/CloudStorage/OneDrive-UniversitàdegliStudidiMilano/Policlinico/analisi/spatial/spatial/K/results_seed/distances_T_mean/Paneth.xlsx',
index=None)
monocyte = pd.DataFrame(data = {'iNKT22 level' : np.concatenate([np.repeat('Low',len(distance_per_cell_type_low_fov_mean['T-Monocyte'])),
np.repeat('High',len(distance_per_cell_type_high_fov_mean['T-Monocyte']))]),
'Distance' : np.concatenate([distance_per_cell_type_low_fov_mean['T-Monocyte'], distance_per_cell_type_high_fov_mean['T-Monocyte']])})
monocyte.to_excel('/Users/paolamaragno/Library/CloudStorage/OneDrive-UniversitàdegliStudidiMilano/Policlinico/analisi/spatial/spatial/K/results_seed/distances_T_mean/Monocyte.xlsx',
index=None)
mast = pd.DataFrame(data = {'iNKT22 level' : np.concatenate([np.repeat('Low',len(distance_per_cell_type_low_fov_mean['T-Mast cell'])),
np.repeat('High',len(distance_per_cell_type_high_fov_mean['T-Mast cell']))]),
'Distance' : np.concatenate([distance_per_cell_type_low_fov_mean['T-Mast cell'], distance_per_cell_type_high_fov_mean['T-Mast cell']])})
mast.to_excel('/Users/paolamaragno/Library/CloudStorage/OneDrive-UniversitàdegliStudidiMilano/Policlinico/analisi/spatial/spatial/K/results_seed/distances_T_mean/Mast.xlsx',
index=None)
goblet = pd.DataFrame(data = {'iNKT22 level' : np.concatenate([np.repeat('Low',len(distance_per_cell_type_low_fov_mean['T-Goblet'])),
np.repeat('High',len(distance_per_cell_type_high_fov_mean['T-Goblet']))]),
'Distance' : np.concatenate([distance_per_cell_type_low_fov_mean['T-Goblet'], distance_per_cell_type_high_fov_mean['T-Goblet']])})
goblet.to_excel('/Users/paolamaragno/Library/CloudStorage/OneDrive-UniversitàdegliStudidiMilano/Policlinico/analisi/spatial/spatial/K/results_seed/distances_T_mean/Goblet.xlsx',
index=None)
CAF = pd.DataFrame(data = {'iNKT22 level' : np.concatenate([np.repeat('Low',len(distance_per_cell_type_low_fov_mean['T-CAF'])),
np.repeat('High',len(distance_per_cell_type_high_fov_mean['CAF-T']))]),
'Distance' : np.concatenate([distance_per_cell_type_low_fov_mean['T-CAF'], distance_per_cell_type_high_fov_mean['CAF-T']])})
CAF.to_excel('/Users/paolamaragno/Library/CloudStorage/OneDrive-UniversitàdegliStudidiMilano/Policlinico/analisi/spatial/spatial/K/results_seed/distances_T_mean/CAF.xlsx',
index=None)
macrophage = pd.DataFrame(data = {'iNKT22 level' : np.concatenate([np.repeat('Low',len(distance_per_cell_type_low_fov_mean['Macrophage-T'])),
np.repeat('High',len(distance_per_cell_type_high_fov_mean['Macrophage-T']))]),
'Distance' : np.concatenate([distance_per_cell_type_low_fov_mean['Macrophage-T'], distance_per_cell_type_high_fov_mean['Macrophage-T']])})
macrophage.to_excel('/Users/paolamaragno/Library/CloudStorage/OneDrive-UniversitàdegliStudidiMilano/Policlinico/analisi/spatial/spatial/K/results_seed/distances_T_mean/Macrophage.xlsx',
index=None)
B = pd.DataFrame(data = {'iNKT22 level' : np.concatenate([np.repeat('Low',len(distance_per_cell_type_low_fov_mean['T-B'])),
np.repeat('High',len(distance_per_cell_type_high_fov_mean['T-B']))]),
'Distance' : np.concatenate([distance_per_cell_type_low_fov_mean['T-B'], distance_per_cell_type_high_fov_mean['T-B']])})
B.to_excel('/Users/paolamaragno/Library/CloudStorage/OneDrive-UniversitàdegliStudidiMilano/Policlinico/analisi/spatial/spatial/K/results_seed/distances_T_mean/B.xlsx',
index=None)
T = pd.DataFrame(data = {'iNKT22 level' : np.concatenate([np.repeat('Low',len(distance_per_cell_type_low_fov_mean['T-T'])),
np.repeat('High',len(distance_per_cell_type_high_fov_mean['T-T']))]),
'Distance' : np.concatenate([distance_per_cell_type_low_fov_mean['T-T'], distance_per_cell_type_high_fov_mean['T-T']])})
T.to_excel('/Users/paolamaragno/Library/CloudStorage/OneDrive-UniversitàdegliStudidiMilano/Policlinico/analisi/spatial/spatial/K/results_seed/distances_T_mean/T.xlsx',
index=None)
plasmaIgA = pd.DataFrame(data = {'iNKT22 level' : np.concatenate([np.repeat('Low',len(distance_per_cell_type_low_fov_mean['T-Plasma IgA'])),
np.repeat('High',len(distance_per_cell_type_high_fov_mean['T-Plasma IgA']))]),
'Distance' : np.concatenate([distance_per_cell_type_low_fov_mean['T-Plasma IgA'], distance_per_cell_type_high_fov_mean['T-Plasma IgA']])})
plasmaIgA.to_excel('/Users/paolamaragno/Library/CloudStorage/OneDrive-UniversitàdegliStudidiMilano/Policlinico/analisi/spatial/spatial/K/results_seed/distances_T_mean/Plasma IgA.xlsx',
index=None)
plasmaIgG = pd.DataFrame(data = {'iNKT22 level' : np.concatenate([np.repeat('Low',len(distance_per_cell_type_low_fov_mean['T-Plasma IgG'])),
np.repeat('High',len(distance_per_cell_type_high_fov_mean['T-Plasma IgG']))]),
'Distance' : np.concatenate([distance_per_cell_type_low_fov_mean['T-Plasma IgG'], distance_per_cell_type_high_fov_mean['T-Plasma IgG']])})
plasmaIgG.to_excel('/Users/paolamaragno/Library/CloudStorage/OneDrive-UniversitàdegliStudidiMilano/Policlinico/analisi/spatial/spatial/K/results_seed/distances_T_mean/Plasma IgG.xlsx',
index=None)
smooth = pd.DataFrame(data = {'iNKT22 level' : np.concatenate([np.repeat('Low',len(distance_per_cell_type_low_fov_mean['T-Smooth muscle'])),
np.repeat('High',len(distance_per_cell_type_high_fov_mean['T-Smooth muscle']))]),
'Distance' : np.concatenate([distance_per_cell_type_low_fov_mean['T-Smooth muscle'], distance_per_cell_type_high_fov_mean['T-Smooth muscle']])})
smooth.to_excel('/Users/paolamaragno/Library/CloudStorage/OneDrive-UniversitàdegliStudidiMilano/Policlinico/analisi/spatial/spatial/K/results_seed/distances_T_mean/Smooth muscle cell.xlsx',
index=None)
endothelial = pd.DataFrame(data = {'iNKT22 level' : np.concatenate([np.repeat('Low',len(distance_per_cell_type_low_fov_mean['T-Endothelial'])),
np.repeat('High',len(distance_per_cell_type_high_fov_mean['T-Endothelial']))]),
'Distance' : np.concatenate([distance_per_cell_type_low_fov_mean['T-Endothelial'], distance_per_cell_type_high_fov_mean['T-Endothelial']])})
endothelial.to_excel('/Users/paolamaragno/Library/CloudStorage/OneDrive-UniversitàdegliStudidiMilano/Policlinico/analisi/spatial/spatial/K/results_seed/distances_T_mean/Endothelial.xlsx',
index=None)
fibro = pd.DataFrame(data = {'iNKT22 level' : np.concatenate([np.repeat('Low',len(distance_per_cell_type_low_fov_mean['T-Fibroblast'])),
np.repeat('High',len(distance_per_cell_type_high_fov_mean['T-Fibroblast']))]),
'Distance' : np.concatenate([distance_per_cell_type_low_fov_mean['T-Fibroblast'], distance_per_cell_type_high_fov_mean['T-Fibroblast']])})
fibro.to_excel('/Users/paolamaragno/Library/CloudStorage/OneDrive-UniversitàdegliStudidiMilano/Policlinico/analisi/spatial/spatial/K/results_seed/distances_T_mean/Fibroblast.xlsx',
index=None)
epi = pd.DataFrame(data = {'iNKT22 level' : np.concatenate([np.repeat('Low',len(distance_per_cell_type_low_fov_mean['T-Epithelial'])),
np.repeat('High',len(distance_per_cell_type_high_fov_mean['T-Epithelial']))]),
'Distance' : np.concatenate([distance_per_cell_type_low_fov_mean['T-Epithelial'], distance_per_cell_type_high_fov_mean['T-Epithelial']])})
epi.to_excel('/Users/paolamaragno/Library/CloudStorage/OneDrive-UniversitàdegliStudidiMilano/Policlinico/analisi/spatial/spatial/K/results_seed/distances_T_mean/Epithelial.xlsx',
index=None)
CAF_CXCL14 = pd.DataFrame(data = {'iNKT22 level' : np.concatenate([np.repeat('Low',len(distance_per_cell_type_low_fov_mean['T-CAF CXCL14'])),
np.repeat('High',len(distance_per_cell_type_high_fov_mean['T-CAF CXCL14']))]),
'Distance' : np.concatenate([distance_per_cell_type_low_fov_mean['T-CAF CXCL14'], distance_per_cell_type_high_fov_mean['T-CAF CXCL14']])})
CAF_CXCL14.to_excel('/Users/paolamaragno/Library/CloudStorage/OneDrive-UniversitàdegliStudidiMilano/Policlinico/analisi/spatial/spatial/K/results_seed/distances_T_mean/CAF CXCL14.xlsx',
index=None)
Compute Ripley’s statistics¶
In [29]:
mode = "L"
sq.gr.ripley(adata_low, cluster_key="leiden", mode=mode)
sq.pl.ripley(
adata_low,
cluster_key="leiden",
mode=mode,
palette = mp.colors.ListedColormap(palette)
)
plt.savefig('/Users/paolamaragno/Library/CloudStorage/OneDrive-UniversitàdegliStudidiMilano/Policlinico/analisi/spatial/spatial/K/results_seed/ripley_low.pdf',bbox_inches="tight")
In [30]:
mode = "L"
sq.gr.ripley(adata_high, cluster_key="leiden", mode=mode)
sq.pl.ripley(
adata_high,
cluster_key="leiden",
mode=mode,
palette = mp.colors.ListedColormap(palette)
)
plt.savefig('/Users/paolamaragno/Library/CloudStorage/OneDrive-UniversitàdegliStudidiMilano/Policlinico/analisi/spatial/spatial/K/results_seed/ripley_high.pdf',bbox_inches="tight")
/opt/anaconda3/envs/spatial_new/lib/python3.9/site-packages/squidpy/gr/_utils.py:194: ImplicitModificationWarning: Trying to modify attribute `._uns` of view, initializing view as actual. obj[key] = data
In [29]:
ripley_per_fov_low = pd.DataFrame(index = pd.unique(adata_low.obs.fov), columns = pd.unique(adata_low.obs.leiden))
In [31]:
mode = "L"
for fov in pd.unique(adata_low.obs.fov):
adata_fov = adata_low[adata_low.obs.fov == fov]
sq.gr.ripley(adata_fov, cluster_key="leiden", mode=mode, max_dist = 500)
a = adata_fov.uns['leiden_ripley_L']['L_stat']
pvalues = []
for inner_list in adata_fov.uns['leiden_ripley_L']['pvalues']:
pvalues.extend(inner_list)
a['p_value'] = pvalues
sub_a = a[a.bins == 500]
for c in pd.unique(adata_low.obs['leiden']):
ripley_per_fov_low.loc[fov,c] = sub_a[sub_a.leiden==c]['stats'].values
/opt/anaconda3/envs/spatial_new/lib/python3.9/site-packages/squidpy/gr/_utils.py:194: ImplicitModificationWarning: Trying to modify attribute `._uns` of view, initializing view as actual. obj[key] = data /opt/anaconda3/envs/spatial_new/lib/python3.9/site-packages/squidpy/gr/_utils.py:194: ImplicitModificationWarning: Trying to modify attribute `._uns` of view, initializing view as actual. obj[key] = data /opt/anaconda3/envs/spatial_new/lib/python3.9/site-packages/squidpy/gr/_utils.py:194: ImplicitModificationWarning: Trying to modify attribute `._uns` of view, initializing view as actual. obj[key] = data /opt/anaconda3/envs/spatial_new/lib/python3.9/site-packages/squidpy/gr/_utils.py:194: ImplicitModificationWarning: Trying to modify attribute `._uns` of view, initializing view as actual. obj[key] = data /opt/anaconda3/envs/spatial_new/lib/python3.9/site-packages/squidpy/gr/_utils.py:194: ImplicitModificationWarning: Trying to modify attribute `._uns` of view, initializing view as actual. obj[key] = data /opt/anaconda3/envs/spatial_new/lib/python3.9/site-packages/squidpy/gr/_utils.py:194: ImplicitModificationWarning: Trying to modify attribute `._uns` of view, initializing view as actual. obj[key] = data /opt/anaconda3/envs/spatial_new/lib/python3.9/site-packages/squidpy/gr/_utils.py:194: ImplicitModificationWarning: Trying to modify attribute `._uns` of view, initializing view as actual. obj[key] = data /opt/anaconda3/envs/spatial_new/lib/python3.9/site-packages/squidpy/gr/_utils.py:194: ImplicitModificationWarning: Trying to modify attribute `._uns` of view, initializing view as actual. obj[key] = data /opt/anaconda3/envs/spatial_new/lib/python3.9/site-packages/squidpy/gr/_utils.py:194: ImplicitModificationWarning: Trying to modify attribute `._uns` of view, initializing view as actual. obj[key] = data /opt/anaconda3/envs/spatial_new/lib/python3.9/site-packages/squidpy/gr/_utils.py:194: ImplicitModificationWarning: Trying to modify attribute `._uns` of view, initializing view as actual. obj[key] = data
In [32]:
import openpyxl
ripley_per_fov_low.to_excel('/Users/paolamaragno/Library/CloudStorage/OneDrive-UniversitàdegliStudidiMilano/Policlinico/analisi/spatial/spatial/K/results_seed/ripley_per_fov_low.xlsx')
In [33]:
ripley_per_fov_high = pd.DataFrame(index = pd.unique(adata_high.obs.fov), columns = pd.unique(adata_high.obs.leiden))
In [35]:
mode = "L"
for fov in pd.unique(adata_high.obs.fov):
adata_fov = adata_high[adata_high.obs.fov == fov]
sq.gr.ripley(adata_fov, cluster_key="leiden", mode=mode, max_dist = 500)
a = adata_fov.uns['leiden_ripley_L']['L_stat']
pvalues = []
for inner_list in adata_fov.uns['leiden_ripley_L']['pvalues']:
pvalues.extend(inner_list)
a['p_value'] = pvalues
sub_a = a[a.bins == 500]
for c in pd.unique(adata_high.obs['leiden']):
ripley_per_fov_high.loc[fov,c] = sub_a[sub_a.leiden==c]['stats'].values
/opt/anaconda3/envs/spatial_new/lib/python3.9/site-packages/squidpy/gr/_utils.py:194: ImplicitModificationWarning: Trying to modify attribute `._uns` of view, initializing view as actual. obj[key] = data /opt/anaconda3/envs/spatial_new/lib/python3.9/site-packages/squidpy/gr/_utils.py:194: ImplicitModificationWarning: Trying to modify attribute `._uns` of view, initializing view as actual. obj[key] = data /opt/anaconda3/envs/spatial_new/lib/python3.9/site-packages/squidpy/gr/_utils.py:194: ImplicitModificationWarning: Trying to modify attribute `._uns` of view, initializing view as actual. obj[key] = data /opt/anaconda3/envs/spatial_new/lib/python3.9/site-packages/squidpy/gr/_utils.py:194: ImplicitModificationWarning: Trying to modify attribute `._uns` of view, initializing view as actual. obj[key] = data /opt/anaconda3/envs/spatial_new/lib/python3.9/site-packages/squidpy/gr/_utils.py:194: ImplicitModificationWarning: Trying to modify attribute `._uns` of view, initializing view as actual. obj[key] = data /opt/anaconda3/envs/spatial_new/lib/python3.9/site-packages/squidpy/gr/_utils.py:194: ImplicitModificationWarning: Trying to modify attribute `._uns` of view, initializing view as actual. obj[key] = data /opt/anaconda3/envs/spatial_new/lib/python3.9/site-packages/squidpy/gr/_utils.py:194: ImplicitModificationWarning: Trying to modify attribute `._uns` of view, initializing view as actual. obj[key] = data
In [36]:
ripley_per_fov_high.to_excel('/Users/paolamaragno/Library/CloudStorage/OneDrive-UniversitàdegliStudidiMilano/Policlinico/analisi/spatial/spatial/K/results_seed/ripley_per_fov_high.xlsx')
In [37]:
pd.unique(adata.obs.fov)
Out[37]:
['1', '4', '5', '6', '7', ..., '16', '17', '18', '19', '20'] Length: 17 Categories (17, object): ['1', '10', '11', '12', ..., '6', '7', '8', '9']
Compute centrality scores¶
In [31]:
degree_per_fov_high = pd.DataFrame(index = pd.unique(adata_high.obs.fov), columns = pd.unique(adata_high.obs.leiden))
average_per_fov_high = pd.DataFrame(index = pd.unique(adata_high.obs.fov), columns = pd.unique(adata_high.obs.leiden))
closeness_per_fov_high = pd.DataFrame(index = pd.unique(adata_high.obs.fov), columns = pd.unique(adata_high.obs.leiden))
In [32]:
degree_per_fov_high
Out[32]:
| Pericyte | CAF | Macrophage | T | Fibroblast | B | Not annotated | Plasma IgG | Smooth muscle | Mast cell | CAF CXCL14 | Endothelial | Monocyte | Paneth | Epithelial | Plasma IgA | Goblet | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 4 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 5 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 6 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 7 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 8 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 12 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 13 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
In [38]:
adata_fov.uns['leiden_centrality_scores']
Out[38]:
| degree_centrality | average_clustering | closeness_centrality | |
|---|---|---|---|
| B | 0.266895 | 0.438926 | 0.407743 |
| CAF | 0.352941 | 0.418114 | 0.507353 |
| CAF CXCL14 | 0.016509 | 0.339683 | 0.130421 |
| Endothelial | 0.253927 | 0.431069 | 0.387162 |
| Fibroblast | 0.162885 | 0.444948 | 0.354700 |
| Macrophage | 0.519161 | 0.429254 | 0.628080 |
| Mast cell | 0.024409 | 0.393333 | 0.151083 |
| Monocyte | 0.035461 | 0.301587 | 0.186262 |
| Not annotated | 0.404514 | 0.435856 | 0.497839 |
| Paneth | 0.003140 | 0.666667 | 0.079615 |
| Pericyte | 0.542105 | 0.427500 | 0.597109 |
| Plasma IgG | 0.185524 | 0.438128 | 0.346298 |
| Smooth muscle | 0.099279 | 0.411538 | 0.237543 |
| T | 0.426273 | 0.435708 | 0.528329 |
In [160]:
sq.gr.spatial_neighbors(
adata2,
radius=300,
coord_type="generic",
)
In [183]:
_, idx = adata2.obsp["spatial_connectivities"][100, :].nonzero()
#idx = np.append(idx, 420)
fig, ax = plt.subplots(1, 2, figsize=(20, 7))
sq.pl.spatial_scatter(
adata2[idx,:],
library_key = 'fov',
library_id=['18'],
connectivity_key="spatial_connectivities",
size=15,
edges_width=0.5,
edges_color="black",
img=False,
ax=ax[0],
title = 'Fov 18 - Low iNKT22'
)
sq.pl.spatial_scatter(
adata2[idx,:],
library_key = 'fov',
library_id=['4'],
color="leiden",
connectivity_key="spatial_connectivities",
size=15,
edges_width=0.5,
edges_color="black",
img=False,
ax=ax[1],
title = 'Fov 4 - High iNKT22'
)
plt.savefig('/Users/paolamaragno/Library/CloudStorage/OneDrive-UniversitàdegliStudidiMilano/Policlinico/analisi/spatial/spatial/K/results_seed/grafh_fov18_4.pdf',bbox_inches="tight")
/opt/anaconda3/envs/spatial_new/lib/python3.9/site-packages/squidpy/pl/_color_utils.py:28: ImplicitModificationWarning: Trying to modify attribute `._uns` of view, initializing view as actual. target.uns[color_key] = source.uns[color_key] /opt/anaconda3/envs/spatial_new/lib/python3.9/site-packages/squidpy/pl/_color_utils.py:28: ImplicitModificationWarning: Trying to modify attribute `._uns` of view, initializing view as actual. target.uns[color_key] = source.uns[color_key]
In [184]:
_, idx = adata2.obsp["spatial_connectivities"][100, :].nonzero()
#idx = np.append(idx, 420)
fig, ax = plt.subplots(1, 2, figsize=(20, 7))
sq.pl.spatial_scatter(
adata2[idx,:],
library_key = 'fov',
library_id=['1'],
connectivity_key="spatial_connectivities",
size=15,
edges_width=0.5,
edges_color="black",
img=False,
ax=ax[0],
title = 'Fov 1 - Low iNKT22'
)
sq.pl.spatial_scatter(
adata2[idx,:],
library_key = 'fov',
library_id=['5'],
color="leiden",
connectivity_key="spatial_connectivities",
size=15,
edges_width=0.5,
edges_color="black",
img=False,
ax=ax[1],
title = 'Fov 5 - High iNKT22'
)
plt.savefig('/Users/paolamaragno/Library/CloudStorage/OneDrive-UniversitàdegliStudidiMilano/Policlinico/analisi/spatial/spatial/K/results_seed/grafh_fov1_5.pdf',bbox_inches="tight")
/opt/anaconda3/envs/spatial_new/lib/python3.9/site-packages/squidpy/pl/_color_utils.py:28: ImplicitModificationWarning: Trying to modify attribute `._uns` of view, initializing view as actual. target.uns[color_key] = source.uns[color_key]
In [46]:
for fov in pd.unique(adata_high.obs.fov):
adata_fov = adata_high[adata_high.obs.fov == fov]
adata_spatial_neighbor = sq.gr.spatial_neighbors(
adata_fov, coord_type="generic", delaunay=True)
sq.gr.centrality_scores(adata_fov, cluster_key="leiden")
a = adata_fov.uns['leiden_centrality_scores']
for c in a.index.values:
degree_per_fov_high.loc[fov,c] = a.loc[c]['degree_centrality']
for c in a.index.values:
average_per_fov_high.loc[fov,c] = a.loc[c]['average_clustering']
for c in a.index.values:
closeness_per_fov_high.loc[fov,c] = a.loc[c]['closeness_centrality']
/opt/anaconda3/envs/spatial_new/lib/python3.9/site-packages/squidpy/gr/_utils.py:194: ImplicitModificationWarning: Setting element `.obsp['spatial_connectivities']` of view, initializing view as actual. obj[key] = data /opt/anaconda3/envs/spatial_new/lib/python3.9/site-packages/squidpy/gr/_utils.py:194: ImplicitModificationWarning: Setting element `.obsp['spatial_connectivities']` of view, initializing view as actual. obj[key] = data /opt/anaconda3/envs/spatial_new/lib/python3.9/site-packages/squidpy/gr/_utils.py:194: ImplicitModificationWarning: Setting element `.obsp['spatial_connectivities']` of view, initializing view as actual. obj[key] = data /opt/anaconda3/envs/spatial_new/lib/python3.9/site-packages/squidpy/gr/_utils.py:194: ImplicitModificationWarning: Setting element `.obsp['spatial_connectivities']` of view, initializing view as actual. obj[key] = data /opt/anaconda3/envs/spatial_new/lib/python3.9/site-packages/squidpy/gr/_utils.py:194: ImplicitModificationWarning: Setting element `.obsp['spatial_connectivities']` of view, initializing view as actual. obj[key] = data /opt/anaconda3/envs/spatial_new/lib/python3.9/site-packages/squidpy/gr/_utils.py:194: ImplicitModificationWarning: Setting element `.obsp['spatial_connectivities']` of view, initializing view as actual. obj[key] = data /opt/anaconda3/envs/spatial_new/lib/python3.9/site-packages/squidpy/gr/_utils.py:194: ImplicitModificationWarning: Setting element `.obsp['spatial_connectivities']` of view, initializing view as actual. obj[key] = data
In [47]:
degree_per_fov_low = pd.DataFrame(index = pd.unique(adata_low.obs.fov), columns = pd.unique(adata_low.obs.leiden))
average_per_fov_low = pd.DataFrame(index = pd.unique(adata_low.obs.fov), columns = pd.unique(adata_low.obs.leiden))
closeness_per_fov_low = pd.DataFrame(index = pd.unique(adata_low.obs.fov), columns = pd.unique(adata_low.obs.leiden))
In [48]:
for fov in pd.unique(adata_low.obs.fov):
adata_fov = adata_low[adata_low.obs.fov == fov]
adata_spatial_neighbor = sq.gr.spatial_neighbors(
adata_fov, coord_type="generic", delaunay=True)
sq.gr.centrality_scores(adata_fov, cluster_key="leiden")
a = adata_fov.uns['leiden_centrality_scores']
for c in a.index.values:
degree_per_fov_low.loc[fov,c] = a.loc[c]['degree_centrality']
for c in a.index.values:
average_per_fov_low.loc[fov,c] = a.loc[c]['average_clustering']
for c in a.index.values:
closeness_per_fov_low.loc[fov,c] = a.loc[c]['closeness_centrality']
/opt/anaconda3/envs/spatial_new/lib/python3.9/site-packages/squidpy/gr/_utils.py:194: ImplicitModificationWarning: Setting element `.obsp['spatial_connectivities']` of view, initializing view as actual. obj[key] = data /opt/anaconda3/envs/spatial_new/lib/python3.9/site-packages/squidpy/gr/_utils.py:194: ImplicitModificationWarning: Setting element `.obsp['spatial_connectivities']` of view, initializing view as actual. obj[key] = data /opt/anaconda3/envs/spatial_new/lib/python3.9/site-packages/squidpy/gr/_utils.py:194: ImplicitModificationWarning: Setting element `.obsp['spatial_connectivities']` of view, initializing view as actual. obj[key] = data /opt/anaconda3/envs/spatial_new/lib/python3.9/site-packages/squidpy/gr/_utils.py:194: ImplicitModificationWarning: Setting element `.obsp['spatial_connectivities']` of view, initializing view as actual. obj[key] = data /opt/anaconda3/envs/spatial_new/lib/python3.9/site-packages/squidpy/gr/_utils.py:194: ImplicitModificationWarning: Setting element `.obsp['spatial_connectivities']` of view, initializing view as actual. obj[key] = data /opt/anaconda3/envs/spatial_new/lib/python3.9/site-packages/squidpy/gr/_utils.py:194: ImplicitModificationWarning: Setting element `.obsp['spatial_connectivities']` of view, initializing view as actual. obj[key] = data /opt/anaconda3/envs/spatial_new/lib/python3.9/site-packages/squidpy/gr/_utils.py:194: ImplicitModificationWarning: Setting element `.obsp['spatial_connectivities']` of view, initializing view as actual. obj[key] = data /opt/anaconda3/envs/spatial_new/lib/python3.9/site-packages/squidpy/gr/_utils.py:194: ImplicitModificationWarning: Setting element `.obsp['spatial_connectivities']` of view, initializing view as actual. obj[key] = data /opt/anaconda3/envs/spatial_new/lib/python3.9/site-packages/squidpy/gr/_utils.py:194: ImplicitModificationWarning: Setting element `.obsp['spatial_connectivities']` of view, initializing view as actual. obj[key] = data /opt/anaconda3/envs/spatial_new/lib/python3.9/site-packages/squidpy/gr/_utils.py:194: ImplicitModificationWarning: Setting element `.obsp['spatial_connectivities']` of view, initializing view as actual. obj[key] = data
In [49]:
degree_per_fov_high.to_excel('/Users/paolamaragno/Library/CloudStorage/OneDrive-UniversitàdegliStudidiMilano/Policlinico/analisi/spatial/spatial/K/results_seed/degree_per_fov_high.xlsx')
average_per_fov_high.to_excel('/Users/paolamaragno/Library/CloudStorage/OneDrive-UniversitàdegliStudidiMilano/Policlinico/analisi/spatial/spatial/K/results_seed/average_per_fov_high.xlsx')
closeness_per_fov_high.to_excel('/Users/paolamaragno/Library/CloudStorage/OneDrive-UniversitàdegliStudidiMilano/Policlinico/analisi/spatial/spatial/K/results_seed/closeness_per_fov_high.xlsx')
In [50]:
degree_per_fov_low.to_excel('/Users/paolamaragno/Library/CloudStorage/OneDrive-UniversitàdegliStudidiMilano/Policlinico/analisi/spatial/spatial/K/results_seed/degree_per_fov_low.xlsx')
average_per_fov_low.to_excel('/Users/paolamaragno/Library/CloudStorage/OneDrive-UniversitàdegliStudidiMilano/Policlinico/analisi/spatial/spatial/K/results_seed/average_per_fov_low.xlsx')
closeness_per_fov_low.to_excel('/Users/paolamaragno/Library/CloudStorage/OneDrive-UniversitàdegliStudidiMilano/Policlinico/analisi/spatial/spatial/K/results_seed/closeness_per_fov_low.xlsx')
Compute co-occurrence probability¶
In [27]:
co_occurrence_per_fov_low = pd.DataFrame(index = pd.unique(adata_low.obs.fov), columns = pd.unique(adata_low.obs.leiden))
co_occurrence_per_fov_high = pd.DataFrame(index = pd.unique(adata_high.obs.fov), columns = pd.unique(adata_high.obs.leiden))
In [39]:
adata_subset = adata2[adata2.obs.fov == "20"].copy()
In [40]:
sq.gr.co_occurrence(
adata_subset,
cluster_key="leiden"
)
sq.pl.co_occurrence(
adata_subset,
cluster_key="leiden"
)
plt.savefig('/Users/paolamaragno/Library/CloudStorage/OneDrive-UniversitàdegliStudidiMilano/Policlinico/analisi/spatial/spatial/K/results_seed/co_occ_ratio_fov20_low.pdf',bbox_inches="tight")
0%| | 0/1 [00:00<?, ?/s]
/opt/anaconda3/envs/spatial_new/lib/python3.9/site-packages/numba/core/decorators.py:246: RuntimeWarning: nopython is set for njit and is ignored
warnings.warn('nopython is set for njit and is ignored', RuntimeWarning)
In [41]:
adata_subset = adata2[adata2.obs.fov == "5"].copy()
In [42]:
sq.gr.co_occurrence(
adata_subset,
cluster_key="leiden"
)
sq.pl.co_occurrence(
adata_subset,
cluster_key="leiden"
)
plt.savefig('/Users/paolamaragno/Library/CloudStorage/OneDrive-UniversitàdegliStudidiMilano/Policlinico/analisi/spatial/spatial/K/results_seed/co_occ_ratio_fov5_high.pdf',bbox_inches="tight")
0%| | 0/1 [00:00<?, ?/s]
/opt/anaconda3/envs/spatial_new/lib/python3.9/site-packages/numba/core/decorators.py:246: RuntimeWarning: nopython is set for njit and is ignored
warnings.warn('nopython is set for njit and is ignored', RuntimeWarning)
Neighboorhod enrichment analysis¶
In [45]:
for fov in pd.unique(adata2.obs.fov):
adata_fov = adata2[adata2.obs.fov == fov]
sq.gr.spatial_neighbors(
adata_fov,
n_neighs=10,
coord_type="generic")
sq.gr.nhood_enrichment(adata_fov, cluster_key="leiden", seed=10)
pd.DataFrame(adata_fov.uns['leiden_nhood_enrichment']['zscore'],
index = np.sort(pd.unique(adata_fov.obs.leiden)), columns = np.sort(pd.unique(adata_fov.obs.leiden))).to_excel('/Users/paolamaragno/Library/CloudStorage/OneDrive-UniversitàdegliStudidiMilano/Policlinico/analisi/spatial/spatial/K/results_seed/neighboorhod/neighboorhod_'+str(fov)+'.xlsx')
/opt/anaconda3/envs/spatial_new/lib/python3.9/site-packages/squidpy/gr/_utils.py:194: ImplicitModificationWarning: Setting element `.obsp['spatial_connectivities']` of view, initializing view as actual. obj[key] = data
0%| | 0/1000 [00:00<?, ?/s]
/opt/anaconda3/envs/spatial_new/lib/python3.9/site-packages/numba/core/decorators.py:246: RuntimeWarning: nopython is set for njit and is ignored
warnings.warn('nopython is set for njit and is ignored', RuntimeWarning)
/opt/anaconda3/envs/spatial_new/lib/python3.9/site-packages/squidpy/gr/_nhood.py:202: RuntimeWarning: invalid value encountered in divide
zscore = (count - perms.mean(axis=0)) / perms.std(axis=0)
/opt/anaconda3/envs/spatial_new/lib/python3.9/site-packages/squidpy/gr/_utils.py:194: ImplicitModificationWarning: Setting element `.obsp['spatial_connectivities']` of view, initializing view as actual.
obj[key] = data
0%| | 0/1000 [00:00<?, ?/s]
/opt/anaconda3/envs/spatial_new/lib/python3.9/site-packages/numba/core/decorators.py:246: RuntimeWarning: nopython is set for njit and is ignored
warnings.warn('nopython is set for njit and is ignored', RuntimeWarning)
/opt/anaconda3/envs/spatial_new/lib/python3.9/site-packages/squidpy/gr/_nhood.py:202: RuntimeWarning: invalid value encountered in divide
zscore = (count - perms.mean(axis=0)) / perms.std(axis=0)
/opt/anaconda3/envs/spatial_new/lib/python3.9/site-packages/squidpy/gr/_utils.py:194: ImplicitModificationWarning: Setting element `.obsp['spatial_connectivities']` of view, initializing view as actual.
obj[key] = data
0%| | 0/1000 [00:00<?, ?/s]
/opt/anaconda3/envs/spatial_new/lib/python3.9/site-packages/numba/core/decorators.py:246: RuntimeWarning: nopython is set for njit and is ignored
warnings.warn('nopython is set for njit and is ignored', RuntimeWarning)
/opt/anaconda3/envs/spatial_new/lib/python3.9/site-packages/squidpy/gr/_utils.py:194: ImplicitModificationWarning: Setting element `.obsp['spatial_connectivities']` of view, initializing view as actual.
obj[key] = data
0%| | 0/1000 [00:00<?, ?/s]
/opt/anaconda3/envs/spatial_new/lib/python3.9/site-packages/numba/core/decorators.py:246: RuntimeWarning: nopython is set for njit and is ignored
warnings.warn('nopython is set for njit and is ignored', RuntimeWarning)
/opt/anaconda3/envs/spatial_new/lib/python3.9/site-packages/squidpy/gr/_nhood.py:202: RuntimeWarning: invalid value encountered in divide
zscore = (count - perms.mean(axis=0)) / perms.std(axis=0)
/opt/anaconda3/envs/spatial_new/lib/python3.9/site-packages/squidpy/gr/_utils.py:194: ImplicitModificationWarning: Setting element `.obsp['spatial_connectivities']` of view, initializing view as actual.
obj[key] = data
0%| | 0/1000 [00:00<?, ?/s]
/opt/anaconda3/envs/spatial_new/lib/python3.9/site-packages/numba/core/decorators.py:246: RuntimeWarning: nopython is set for njit and is ignored
warnings.warn('nopython is set for njit and is ignored', RuntimeWarning)
/opt/anaconda3/envs/spatial_new/lib/python3.9/site-packages/squidpy/gr/_nhood.py:202: RuntimeWarning: invalid value encountered in divide
zscore = (count - perms.mean(axis=0)) / perms.std(axis=0)
/opt/anaconda3/envs/spatial_new/lib/python3.9/site-packages/squidpy/gr/_utils.py:194: ImplicitModificationWarning: Setting element `.obsp['spatial_connectivities']` of view, initializing view as actual.
obj[key] = data
0%| | 0/1000 [00:00<?, ?/s]
/opt/anaconda3/envs/spatial_new/lib/python3.9/site-packages/numba/core/decorators.py:246: RuntimeWarning: nopython is set for njit and is ignored
warnings.warn('nopython is set for njit and is ignored', RuntimeWarning)
/opt/anaconda3/envs/spatial_new/lib/python3.9/site-packages/squidpy/gr/_nhood.py:202: RuntimeWarning: invalid value encountered in divide
zscore = (count - perms.mean(axis=0)) / perms.std(axis=0)
/opt/anaconda3/envs/spatial_new/lib/python3.9/site-packages/squidpy/gr/_utils.py:194: ImplicitModificationWarning: Setting element `.obsp['spatial_connectivities']` of view, initializing view as actual.
obj[key] = data
0%| | 0/1000 [00:00<?, ?/s]
/opt/anaconda3/envs/spatial_new/lib/python3.9/site-packages/numba/core/decorators.py:246: RuntimeWarning: nopython is set for njit and is ignored
warnings.warn('nopython is set for njit and is ignored', RuntimeWarning)
/opt/anaconda3/envs/spatial_new/lib/python3.9/site-packages/squidpy/gr/_nhood.py:202: RuntimeWarning: invalid value encountered in divide
zscore = (count - perms.mean(axis=0)) / perms.std(axis=0)
/opt/anaconda3/envs/spatial_new/lib/python3.9/site-packages/squidpy/gr/_utils.py:194: ImplicitModificationWarning: Setting element `.obsp['spatial_connectivities']` of view, initializing view as actual.
obj[key] = data
0%| | 0/1000 [00:00<?, ?/s]
/opt/anaconda3/envs/spatial_new/lib/python3.9/site-packages/numba/core/decorators.py:246: RuntimeWarning: nopython is set for njit and is ignored
warnings.warn('nopython is set for njit and is ignored', RuntimeWarning)
/opt/anaconda3/envs/spatial_new/lib/python3.9/site-packages/squidpy/gr/_nhood.py:202: RuntimeWarning: invalid value encountered in divide
zscore = (count - perms.mean(axis=0)) / perms.std(axis=0)
/opt/anaconda3/envs/spatial_new/lib/python3.9/site-packages/squidpy/gr/_utils.py:194: ImplicitModificationWarning: Setting element `.obsp['spatial_connectivities']` of view, initializing view as actual.
obj[key] = data
0%| | 0/1000 [00:00<?, ?/s]
/opt/anaconda3/envs/spatial_new/lib/python3.9/site-packages/numba/core/decorators.py:246: RuntimeWarning: nopython is set for njit and is ignored
warnings.warn('nopython is set for njit and is ignored', RuntimeWarning)
/opt/anaconda3/envs/spatial_new/lib/python3.9/site-packages/squidpy/gr/_nhood.py:202: RuntimeWarning: invalid value encountered in divide
zscore = (count - perms.mean(axis=0)) / perms.std(axis=0)
/opt/anaconda3/envs/spatial_new/lib/python3.9/site-packages/squidpy/gr/_utils.py:194: ImplicitModificationWarning: Setting element `.obsp['spatial_connectivities']` of view, initializing view as actual.
obj[key] = data
0%| | 0/1000 [00:00<?, ?/s]
/opt/anaconda3/envs/spatial_new/lib/python3.9/site-packages/numba/core/decorators.py:246: RuntimeWarning: nopython is set for njit and is ignored
warnings.warn('nopython is set for njit and is ignored', RuntimeWarning)
/opt/anaconda3/envs/spatial_new/lib/python3.9/site-packages/squidpy/gr/_utils.py:194: ImplicitModificationWarning: Setting element `.obsp['spatial_connectivities']` of view, initializing view as actual.
obj[key] = data
0%| | 0/1000 [00:00<?, ?/s]
/opt/anaconda3/envs/spatial_new/lib/python3.9/site-packages/numba/core/decorators.py:246: RuntimeWarning: nopython is set for njit and is ignored
warnings.warn('nopython is set for njit and is ignored', RuntimeWarning)
/opt/anaconda3/envs/spatial_new/lib/python3.9/site-packages/squidpy/gr/_utils.py:194: ImplicitModificationWarning: Setting element `.obsp['spatial_connectivities']` of view, initializing view as actual.
obj[key] = data
0%| | 0/1000 [00:00<?, ?/s]
/opt/anaconda3/envs/spatial_new/lib/python3.9/site-packages/numba/core/decorators.py:246: RuntimeWarning: nopython is set for njit and is ignored
warnings.warn('nopython is set for njit and is ignored', RuntimeWarning)
/opt/anaconda3/envs/spatial_new/lib/python3.9/site-packages/squidpy/gr/_nhood.py:202: RuntimeWarning: invalid value encountered in divide
zscore = (count - perms.mean(axis=0)) / perms.std(axis=0)
/opt/anaconda3/envs/spatial_new/lib/python3.9/site-packages/squidpy/gr/_utils.py:194: ImplicitModificationWarning: Setting element `.obsp['spatial_connectivities']` of view, initializing view as actual.
obj[key] = data
0%| | 0/1000 [00:00<?, ?/s]
/opt/anaconda3/envs/spatial_new/lib/python3.9/site-packages/numba/core/decorators.py:246: RuntimeWarning: nopython is set for njit and is ignored
warnings.warn('nopython is set for njit and is ignored', RuntimeWarning)
/opt/anaconda3/envs/spatial_new/lib/python3.9/site-packages/squidpy/gr/_nhood.py:202: RuntimeWarning: invalid value encountered in divide
zscore = (count - perms.mean(axis=0)) / perms.std(axis=0)
/opt/anaconda3/envs/spatial_new/lib/python3.9/site-packages/squidpy/gr/_utils.py:194: ImplicitModificationWarning: Setting element `.obsp['spatial_connectivities']` of view, initializing view as actual.
obj[key] = data
0%| | 0/1000 [00:00<?, ?/s]
/opt/anaconda3/envs/spatial_new/lib/python3.9/site-packages/numba/core/decorators.py:246: RuntimeWarning: nopython is set for njit and is ignored
warnings.warn('nopython is set for njit and is ignored', RuntimeWarning)
/opt/anaconda3/envs/spatial_new/lib/python3.9/site-packages/squidpy/gr/_nhood.py:202: RuntimeWarning: invalid value encountered in divide
zscore = (count - perms.mean(axis=0)) / perms.std(axis=0)
/opt/anaconda3/envs/spatial_new/lib/python3.9/site-packages/squidpy/gr/_utils.py:194: ImplicitModificationWarning: Setting element `.obsp['spatial_connectivities']` of view, initializing view as actual.
obj[key] = data
0%| | 0/1000 [00:00<?, ?/s]
/opt/anaconda3/envs/spatial_new/lib/python3.9/site-packages/numba/core/decorators.py:246: RuntimeWarning: nopython is set for njit and is ignored
warnings.warn('nopython is set for njit and is ignored', RuntimeWarning)
/opt/anaconda3/envs/spatial_new/lib/python3.9/site-packages/squidpy/gr/_nhood.py:202: RuntimeWarning: invalid value encountered in divide
zscore = (count - perms.mean(axis=0)) / perms.std(axis=0)
/opt/anaconda3/envs/spatial_new/lib/python3.9/site-packages/squidpy/gr/_utils.py:194: ImplicitModificationWarning: Setting element `.obsp['spatial_connectivities']` of view, initializing view as actual.
obj[key] = data
0%| | 0/1000 [00:00<?, ?/s]
/opt/anaconda3/envs/spatial_new/lib/python3.9/site-packages/numba/core/decorators.py:246: RuntimeWarning: nopython is set for njit and is ignored
warnings.warn('nopython is set for njit and is ignored', RuntimeWarning)
/opt/anaconda3/envs/spatial_new/lib/python3.9/site-packages/squidpy/gr/_nhood.py:202: RuntimeWarning: invalid value encountered in divide
zscore = (count - perms.mean(axis=0)) / perms.std(axis=0)
/opt/anaconda3/envs/spatial_new/lib/python3.9/site-packages/squidpy/gr/_utils.py:194: ImplicitModificationWarning: Setting element `.obsp['spatial_connectivities']` of view, initializing view as actual.
obj[key] = data
0%| | 0/1000 [00:00<?, ?/s]
/opt/anaconda3/envs/spatial_new/lib/python3.9/site-packages/numba/core/decorators.py:246: RuntimeWarning: nopython is set for njit and is ignored
warnings.warn('nopython is set for njit and is ignored', RuntimeWarning)
Novae¶
First, we need to load a pretrained Novae model.
Since we have a human colon slide, we load the "MICS-Lab/novae-human-0" model. Other model names can be found here (spot-resolution models, e.g. for Visium data, will come soon).
In [25]:
import novae
/opt/anaconda3/envs/spatial_new/lib/python3.9/site-packages/torch_geometric/typing.py:86: UserWarning: An issue occurred while importing 'torch-scatter'. Disabling its usage. Stacktrace: dlopen(/opt/anaconda3/envs/spatial_new/lib/python3.9/site-packages/torch_scatter/_version_cpu.so, 0x0006): Symbol not found: __ZN3c1017RegisterOperatorsD1Ev Referenced from: <D18F459C-0B72-3445-A171-323DA250B6F5> /opt/anaconda3/envs/spatial_new/lib/python3.9/site-packages/torch_scatter/_version_cpu.so Expected in: <36F46DB8-DB62-3926-8653-E332C34252FB> /opt/anaconda3/envs/spatial_new/lib/python3.9/site-packages/torch/lib/libtorch_cpu.dylib warnings.warn(f"An issue occurred while importing 'torch-scatter'. " /opt/anaconda3/envs/spatial_new/lib/python3.9/site-packages/torch_geometric/typing.py:124: UserWarning: An issue occurred while importing 'torch-sparse'. Disabling its usage. Stacktrace: dlopen(/opt/anaconda3/envs/spatial_new/lib/python3.9/site-packages/torch_sparse/_version_cpu.so, 0x0006): Symbol not found: __ZN3c1017RegisterOperatorsD1Ev Referenced from: <C4CABDA1-9DE9-35D3-97B9-7BCB2C0E1E9E> /opt/anaconda3/envs/spatial_new/lib/python3.9/site-packages/torch_sparse/_version_cpu.so Expected in: <36F46DB8-DB62-3926-8653-E332C34252FB> /opt/anaconda3/envs/spatial_new/lib/python3.9/site-packages/torch/lib/libtorch_cpu.dylib warnings.warn(f"An issue occurred while importing 'torch-sparse'. "
In [26]:
model = novae.Novae.from_pretrained("MICS-Lab/novae-human-0")
model
Out[26]:
Novae model ├── Known genes: 60697 ├── Parameters: 32.0M └── Model name: MICS-Lab/novae-human-0
Create a Delaunay graph from the spatial coordinates of the cells (in microns). The graph is stored in adata.obsp['spatial_connectivities'] and adata.obsp['spatial_distances']. The long edges are removed from the graph according to the radius argument (if provided). The spatial coordinates are expected to be in microns, and stored in adata.obsm["spatial"]. If the coordinates are in pixels, set pixel_size to the size of a pixel in microns. If you don't know the pixel_size, or if you don't have adata.obsm["spatial"], you can also provide the technology argument.
In [27]:
# this step because the spatial coordinates are not in micron but in pixel
novae.utils.spatial_neighbors(adata2, slide_key='fov',technology='cosmx')
[INFO] (novae.utils._build) Computing delaunay graph on 26,757 cells (radius threshold: [0.0, 100.0] microns)
Now, we can compute the spatial representations for each cell. In the first option below, we pass the argument zero_shot=True to run only inference (i.e., the model is not re-trained).
Instead of zero-shot, if you want to fine-tune the model, you can use the fine_tune method, and then call compute_representations without the zero_shot argument (see "option 2").
In [28]:
# Option 1: zero-shot
model.compute_representations(adata2, zero_shot=True)
# Option 2: fine-tuning
#model.fine_tune(adata)
#model.compute_representations(adata)
Computing representations: 0%| | 0/53 [00:00<?, ?it/s]
To assign domains, you can use the assign_domains method, as below. By default, it creates 7 domains, but you can choose the number of domains you want with the level argument.
The function will save the domains in adata.obs, and return the name of the column in which it was saved
In [29]:
model.assign_domains(adata2, level=15)
Out[29]:
'novae_domains_15'
Then, to show the results, you can use novae.plot.domains. If you run model.assign_domains multiple times, you can also decide the resolution you want to show, by passing the obs_key argument to novae.plot.domains.
In [111]:
novae.plot.domains(adata2, library_id=None)
[INFO] (novae.utils._validate) Using obs_key='novae_domains_20' as default.
In [30]:
palette = ['#0F2080', '#006600', '#AA3377', '#BBBBBB', '#AEC7E8', '#CCBB44', '#F7B6D2','#000000',
'#0077BB', '#EE7733', '#33BBEE', '#CC3311', '#7F7F7F', '#FFB000','#601A4A','#785EF0','#66CC00', '#CCFF99',
'#660000', '#E5CCFF','#FFFFCC','#CCFFE5']
sns.color_palette(palette)
Out[30]:
In [122]:
sc.pl.umap(
adata2, show=False,
color= "novae_domains_15",
palette=sns.color_palette(palette)
)
Out[122]:
<Axes: title={'center': 'novae_domains_15'}, xlabel='UMAP1', ylabel='UMAP2'>
In [133]:
t = ['fov 1 - Patient_1 - LOW',
'fov 10 - Patient_6 - LOW',
'fov 11 - Patient_6 - LOW',
'fov 12 - Patient_7 - HIGH',
'fov 13 - Patient_7 - HIGH',
'fov 14 - Patient_8 - LOW',
'fov 16 - Patient_8 - LOW',
'fov 17 - Patient_8 - LOW',
'fov 18 - Patient_9 - LOW',
'fov 19 - Patient_9 - LOW',
'fov 20 - Patient_9 - LOW',
'fov 4 - Patient_2 - HIGH',
'fov 5 - Patient_2 - HIGH',
'fov 6 - Patient_4 - HIGH',
'fov 7 - Patient_4 - HIGH',
'fov 8 - Patient_4 - HIGH',
'fov 9 - Patient_6 - LOW']
sq.pl.spatial_segment(
adata=adata2,
color='novae_domains_15',
library_key="fov",
seg_cell_id="cell_ID",
seg_outline=True,
img=False,
scalebar_dx=1.0,
title= t,
scalebar_kwargs={"scale_loc": "bottom", "location": "lower right"},
)
plt.savefig('/Users/paolamaragno/Library/CloudStorage/OneDrive-UniversitàdegliStudidiMilano/Policlinico/analisi/spatial/spatial/K/results_seed/niche_all_fov_novae_15domains.pdf',bbox_inches="tight")
In [31]:
adata2.obs.to_csv('/Users/paolamaragno/Library/CloudStorage/OneDrive-UniversitàdegliStudidiMilano/Policlinico/analisi/spatial/spatial/K/results_seed/metadata_adata_niche_novae_15domains.csv')
In [32]:
# Import JSON module
import json
f = open("/Users/paolamaragno/Downloads/h.all.v2024.1.Hs.json")
data = json.load(f)
In [33]:
data = {key: data[key] for key in ['HALLMARK_TNFA_SIGNALING_VIA_NFKB','HALLMARK_WNT_BETA_CATENIN_SIGNALING','HALLMARK_TGF_BETA_SIGNALING','HALLMARK_IL6_JAK_STAT3_SIGNALING',
'HALLMARK_DNA_REPAIR','HALLMARK_G2M_CHECKPOINT','HALLMARK_APOPTOSIS','HALLMARK_NOTCH_SIGNALING','HALLMARK_MYOGENESIS','HALLMARK_PROTEIN_SECRETION',
'HALLMARK_INTERFERON_ALPHA_RESPONSE','HALLMARK_INTERFERON_GAMMA_RESPONSE','HALLMARK_APICAL_JUNCTION','HALLMARK_APICAL_SURFACE','HALLMARK_HEDGEHOG_SIGNALING',
'HALLMARK_COMPLEMENT','HALLMARK_UNFOLDED_PROTEIN_RESPONSE','HALLMARK_PI3K_AKT_MTOR_SIGNALING','HALLMARK_E2F_TARGETS','HALLMARK_MYC_TARGETS_V1',
'HALLMARK_MYC_TARGETS_V2','HALLMARK_EPITHELIAL_MESENCHYMAL_TRANSITION','HALLMARK_INFLAMMATORY_RESPONSE','HALLMARK_XENOBIOTIC_METABOLISM',
'HALLMARK_OXIDATIVE_PHOSPHORYLATION','HALLMARK_GLYCOLYSIS','HALLMARK_REACTIVE_OXYGEN_SPECIES_PATHWAY','HALLMARK_P53_PATHWAY','HALLMARK_ANGIOGENESIS',
'HALLMARK_HEME_METABOLISM','HALLMARK_COAGULATION','HALLMARK_IL2_STAT5_SIGNALING','HALLMARK_PEROXISOME','HALLMARK_KRAS_SIGNALING_UP',
'HALLMARK_KRAS_SIGNALING_DN','HALLMARK_MTORC1_SIGNALING']}
In [34]:
pathways = {}
for k in data.keys():
pathways[k] = data[k]['geneSymbols']
In [35]:
novae.plot.pathway_scores(adata2, obs_key ="novae_domains_15",pathways=pathways, figsize=(15, 7))
plt.savefig('/Users/paolamaragno/Library/CloudStorage/OneDrive-UniversitàdegliStudidiMilano/Policlinico/analisi/spatial/spatial/K/results_seed/pathways_novae_15domains.pdf',bbox_inches="tight")
/opt/anaconda3/envs/spatial_new/lib/python3.9/site-packages/novae/plot/_heatmap.py:111: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning. df = df.groupby(obs_key).mean()
DEGs between niches¶
In [69]:
adata_niche_D1004 = adata2[adata2.obs.novae_domains_15 =='D1004']
adata_niche_D1005 = adata2[adata2.obs.novae_domains_15 =='D1005']
adata_niche_D1007 = adata2[adata2.obs.novae_domains_15 =='D1007']
adata_niche_D961 = adata2[adata2.obs.novae_domains_15 =='D961']
adata_niche_D991 = adata2[adata2.obs.novae_domains_15 =='D991']
adata_niche_D994 = adata2[adata2.obs.novae_domains_15 =='D994']
adata_niche_D998 = adata2[adata2.obs.novae_domains_15 =='D998']
In [70]:
sc.tl.rank_genes_groups(adata_niche_D1004, 'level', groups=['LOW'], reference='HIGH', method='wilcoxon')
adata_niche_D1004 = sc.get.rank_genes_groups_df(adata_niche_D1004, group='LOW',key = "rank_genes_groups")
adata_niche_D1004.to_excel('/Users/paolamaragno/Library/CloudStorage/OneDrive-UniversitàdegliStudidiMilano/Policlinico/analisi/spatial/spatial/K/results_seed/DEGs_niche_D1004.xlsx')
sc.tl.rank_genes_groups(adata_niche_D1005, 'level', groups=['LOW'], reference='HIGH', method='wilcoxon')
adata_niche_D1005 = sc.get.rank_genes_groups_df(adata_niche_D1005, group='LOW',key = "rank_genes_groups")
adata_niche_D1005.to_excel('/Users/paolamaragno/Library/CloudStorage/OneDrive-UniversitàdegliStudidiMilano/Policlinico/analisi/spatial/spatial/K/results_seed/DEGs_niche_D1005.xlsx')
sc.tl.rank_genes_groups(adata_niche_D1007, 'level', groups=['LOW'], reference='HIGH', method='wilcoxon')
adata_niche_D1007 = sc.get.rank_genes_groups_df(adata_niche_D1007, group='LOW',key = "rank_genes_groups")
adata_niche_D1007.to_excel('/Users/paolamaragno/Library/CloudStorage/OneDrive-UniversitàdegliStudidiMilano/Policlinico/analisi/spatial/spatial/K/results_seed/DEGs_niche_D1007.xlsx')
#sc.tl.rank_genes_groups(adata_niche_D961, 'level', groups=['LOW'], reference='HIGH', method='wilcoxon')
#adata_niche_D961 = sc.get.rank_genes_groups_df(adata_niche_D961, group='LOW',key = "rank_genes_groups")
#adata_niche_D961.to_excel('/Users/paolamaragno/Library/CloudStorage/OneDrive-UniversitàdegliStudidiMilano/Policlinico/analisi/spatial/spatial/K/results_seed/DEGs_niche_D961.xlsx')
sc.tl.rank_genes_groups(adata_niche_D991, 'level', groups=['LOW'], reference='HIGH', method='wilcoxon')
adata_niche_D991 = sc.get.rank_genes_groups_df(adata_niche_D991, group='LOW',key = "rank_genes_groups")
adata_niche_D991.to_excel('/Users/paolamaragno/Library/CloudStorage/OneDrive-UniversitàdegliStudidiMilano/Policlinico/analisi/spatial/spatial/K/results_seed/DEGs_niche_D991.xlsx')
#sc.tl.rank_genes_groups(adata_niche_D994, 'level', groups=['LOW'], reference='HIGH', method='wilcoxon')
#adata_niche_D994 = sc.get.rank_genes_groups_df(adata_niche_D994, group='LOW',key = "rank_genes_groups")
#adata_niche_D994.to_excel('/Users/paolamaragno/Library/CloudStorage/OneDrive-UniversitàdegliStudidiMilano/Policlinico/analisi/spatial/spatial/K/results_seed/DEGs_niche_D994.xlsx')
sc.tl.rank_genes_groups(adata_niche_D998, 'level', groups=['LOW'], reference='HIGH', method='wilcoxon')
adata_niche_D998 = sc.get.rank_genes_groups_df(adata_niche_D998, group='LOW',key = "rank_genes_groups")
adata_niche_D998.to_excel('/Users/paolamaragno/Library/CloudStorage/OneDrive-UniversitàdegliStudidiMilano/Policlinico/analisi/spatial/spatial/K/results_seed/DEGs_niche_D998.xlsx')
/opt/anaconda3/envs/spatial_new/lib/python3.9/site-packages/anndata/_core/anndata.py:1145: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual. df[key] = c /opt/anaconda3/envs/spatial_new/lib/python3.9/site-packages/anndata/_core/anndata.py:1145: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual. df[key] = c /opt/anaconda3/envs/spatial_new/lib/python3.9/site-packages/anndata/_core/anndata.py:1145: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual. df[key] = c /opt/anaconda3/envs/spatial_new/lib/python3.9/site-packages/anndata/_core/anndata.py:1145: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual. df[key] = c /opt/anaconda3/envs/spatial_new/lib/python3.9/site-packages/anndata/_core/anndata.py:1145: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual. df[key] = c
In [65]:
adata_niche_D1004_exp = pd.DataFrame(adata_niche_D1004.X.toarray(), index = adata_niche_D1004.obs.index.values, columns = adata_niche_D1004.var.index.values)
adata_niche_D1004_exp['level'] = adata_niche_D1004.obs.level
In [66]:
adata_niche_D1004_exp
Out[66]:
| RAMP2 | CD83 | RYK | CD5L | NLRP2 | BIRC5 | LIF | H2AZ1 | PTGES3 | CDH11 | ... | PXDN | RGS13 | SLA | SLCO2B1 | SQLE | SRSF2 | TGFBI | TPI1 | ZBTB16 | level | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1_1 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.00000 | 0.0 | 0.000000 | 0.000000 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.000000 | 0.000000 | 0.000000 | 0.0 | LOW |
| 4_1 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.00000 | 0.0 | 0.000000 | 0.000000 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.000000 | 0.000000 | 0.000000 | 0.0 | LOW |
| 5_1 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.00000 | 0.0 | 0.000000 | 0.000000 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.000000 | 0.756014 | 1.181587 | 0.0 | LOW |
| 20_1 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.00000 | 0.0 | 0.457673 | 0.457673 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.008387 | 0.000000 | 0.770471 | 0.0 | LOW |
| 23_1 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.00000 | 0.0 | 0.000000 | 0.321008 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.000000 | 0.321008 | 0.000000 | 0.0 | LOW |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 1688_20 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.00000 | 0.0 | 0.000000 | 0.000000 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.000000 | 0.000000 | 0.000000 | 0.0 | LOW |
| 1692_20 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.00000 | 0.0 | 0.000000 | 0.000000 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.000000 | 0.000000 | 0.000000 | 0.0 | LOW |
| 1693_20 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.00000 | 0.0 | 0.000000 | 0.000000 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.000000 | 1.808854 | 0.000000 | 0.0 | LOW |
| 1694_20 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.00000 | 0.0 | 0.000000 | 0.000000 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.000000 | 1.422852 | 0.000000 | 0.0 | LOW |
| 1698_20 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.95885 | 0.0 | 0.000000 | 0.000000 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.000000 | 0.000000 | 0.000000 | 0.0 | LOW |
2425 rows × 993 columns
In [68]:
adata_niche_D1004_exp.groupby('level', as_index=False)['IL6'].mean()
/var/folders/xx/sgggrhp55xx_8s1thvzkgr600000gn/T/ipykernel_1674/3999256133.py:1: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
adata_niche_D1004_exp.groupby('level', as_index=False)['IL6'].mean()
Out[68]:
| level | IL6 | |
|---|---|---|
| 0 | HIGH | 0.254628 |
| 1 | LOW | 0.033637 |